diff --git a/ErrorAnalysis/ErrorAnalysis.ipynb b/ErrorAnalysis/ErrorAnalysis.ipynb
index d65a16d4bc9fdeb619b2b170053d2808739913d1..ca3b52e9fdf58dfbb846678fe6e7c755074dcb6e 100644
--- a/ErrorAnalysis/ErrorAnalysis.ipynb
+++ b/ErrorAnalysis/ErrorAnalysis.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,11 +47,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fname=\"LevelCurveData\"\n",
+    "fname=\"LevelCurveData2\"\n",
     "colorsequence=['b', 'g', 'r', 'c', 'm', 'y', 'k']"
    ]
   },
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +166,7 @@
       "29       0.928257          NaN         NaN       95.577034  \n",
       "30       0.864804     0.828064    0.136632       92.172024  \n",
       "31       0.894401     0.694871    0.122168       93.758843  \n",
-      "32       0.912729     0.657580    0.146093       94.808241  \n",
+      "32       0.912728     0.657580    0.146093       94.808241  \n",
       "33       0.915191     0.668287    0.175769       94.929875  \n",
       "34       0.921779          NaN         NaN       95.343800  \n",
       "35       0.921254          NaN         NaN       95.344421  \n",
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +376,7 @@
        "4 40   4000       0.878487     0.594085    0.117988       91.222850  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -469,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,91 +522,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0.8388654327627629, 26.63803139771705), (0.851687514954323, 26.958007515716748), (0.8650419351690942, 26.952958843064685), (0.8725219776895965, 26.990672702260014), (0.8784872660775024, 27.039436594751677), (0.8826995277177598, 27.107602503295283), (0.8852063376852094, 27.20029112619097), (0.8872804729674099, 27.313105633909874), (0.8878465633146485, 27.47106940880744), (0.8952729857129805, 27.645194093681265)]\n",
+      "[(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: 1.7465851504021037e-05\n",
-      "   maxcv: 3.204926956869903e-20\n",
+      "     fun: 3.9907231233324056e-05\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([ 2.66347675e+01,  3.22010981e-01, -3.20492696e-20,  3.22751269e-02,\n",
-      "        5.14609460e-02,  6.60504354e-02,  9.49331500e-02,  1.10501223e-01,\n",
-      "        1.59678761e-01,  1.72998698e-01])\n",
-      "[26.63476755 26.95677853 26.95677853 26.98905366 27.0405146  27.10656504\n",
-      " 27.20149819 27.31199941 27.47167817 27.64467687]\n",
-      "[(0.8480086590097955, 25.36197487949973), (0.8663548974850813, 25.66748831383359), (0.8839022026959875, 25.591666460155107), (0.8942064579237392, 25.597565626508263), (0.8962998659218736, 25.726619578670263), (0.8997734472491155, 25.831229954294248), (0.9022597468179228, 26.009027804741805), (0.9051879229336092, 26.513574437011215), (0.906129528841706, 26.131917995570717), (0.9100540269577614, 26.576390813456225)]\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",
       "10\n",
-      "     fun: 0.038195812021937975\n",
-      "   maxcv: 7.32657626504511e-20\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([ 2.53610541e+01,  2.58373558e-01, -7.29072292e-20, -7.29188180e-20,\n",
-      "        1.06238291e-01,  1.06229834e-01,  1.77143082e-01,  3.13464918e-01,\n",
-      "       -7.32657627e-20,  2.53722707e-01])\n",
-      "[25.36105412 25.61942768 25.61942768 25.61942768 25.72566597 25.83189581\n",
-      " 26.00903889 26.32250381 26.32250381 26.57622651]\n",
-      "[(0.8582278355670888, 24.401842511771523), (0.8936744459824406, 24.343229865424878), (0.9013143984785664, 24.57246012806053), (0.908879226767728, 24.723011401582752), (0.9120431759673145, 24.903298316153407), (0.916585803861727, 25.128870597103948), (0.9182241643510174, 25.307036037160085), (0.923680152937285, 25.425537392231607), (0.9244442654917396, 25.715511558804522), (0.9282573406208912, 25.894979424435977)]\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",
       "10\n",
-      "     fun: 0.0010256981152863887\n",
-      "   maxcv: 2.1590737733573267e-19\n",
+      "     fun: 0.0011194936676139564\n",
+      "   maxcv: 5.421010862427523e-20\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.43746097e+01, -2.15907377e-19,  1.89455692e-01,  1.64086799e-01,\n",
-      "        1.71869606e-01,  2.36170026e-01,  1.62588905e-01,  1.34710672e-01,\n",
-      "        2.76667136e-01,  1.86762438e-01])\n",
-      "[24.37460975 24.37460975 24.56406544 24.72815224 24.90002184 25.13619187\n",
-      " 25.29878077 25.43349145 25.71015858 25.89692102]\n",
-      "[(0.8648039548726123, 23.53705497300391), (0.8944010552242152, 23.756524428006948), (0.9127285001068114, 23.691146425415372), (0.9151909375374808, 23.988770696234344), (0.9198208152035846, 25.05158084398013), (0.9199216659194108, 24.72009516487679), (0.9212544556078396, 24.38145483647575), (0.9215534495709452, 25.573799364708613), (0.9217793266014028, 24.12994877031085), (0.9220035013843578, 25.220248779641423)]\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",
       "10\n",
-      "     fun: 0.6345199957693715\n",
-      "   maxcv: 3.557706235855446e-20\n",
-      " message: 'Maximum number of function evaluations has been exceeded.'\n",
-      "    nfev: 1000\n",
-      "  status: 2\n",
-      " success: False\n",
-      "       x: array([ 2.35352481e+01,  1.89599956e-01, -3.55770624e-20,  2.62517981e-01,\n",
-      "        7.30581550e-01, -2.51205908e-20, -2.80457617e-20,  1.33977447e-01,\n",
-      "       -3.26325035e-20,  3.68715097e-01])\n",
-      "[23.53524811 23.72484807 23.72484807 23.98736605 24.7179476  24.7179476\n",
-      " 24.7179476  24.85192505 24.85192505 25.22064014]\n",
-      "[(0.8812751435156017, 22.636278271387898), (0.9104357215404324, 22.78596344770866), (0.919387967849143, 23.08736296153922), (0.9240519831368692, 23.298403361291445), (0.9287888212620552, 23.535499082978905), (0.9337104164968394, 23.703208733677567), (0.9342045774665652, 24.818266151599808), (0.934618933958842, 24.401882615429468), (0.9356992871040192, 25.00547558932549), (0.936971005073724, 23.90587189010079)]\n",
+      "     fun: 0.6345178126830088\n",
+      "   maxcv: 9.958200192064222e-20\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 838\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",
       "10\n",
-      "     fun: 0.3581002896083334\n",
-      "   maxcv: 1.764117320315744e-19\n",
+      "     fun: 0.35779439724992584\n",
+      "   maxcv: 1.0373675707956813e-19\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.26138615e+01,  1.95129462e-01,  2.72433790e-01,  2.16030927e-01,\n",
-      "        2.43502260e-01,  1.59182791e-01,  8.33257226e-01, -1.74562499e-19,\n",
-      "       -1.76411732e-19, -1.64932643e-19])\n",
-      "[22.6138615  22.80899096 23.08142475 23.29745568 23.54095793 23.70014073\n",
-      " 24.53339795 24.53339795 24.53339795 24.53339795]\n",
-      "[(0.8851190690740085, 21.946046310304236), (0.9178129817676176, 22.32535595505711), (0.9295563275839092, 22.62522228432957), (0.9328199304429364, 23.265542877435518), (0.935662702280138, 24.027407736896695), (0.9364706301026184, 24.801416841360897), (0.9368171040431896, 24.50401453438995), (0.937286512283342, 23.58883198125333), (0.9429635158559566, 24.8451137482077), (0.9441767767768364, 25.35023202841469)]\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",
       "10\n",
-      "     fun: 0.39998989464849644\n",
-      "   maxcv: 1.9156791606209056e-19\n",
+      "     fun: 0.3994864023989464\n",
+      "   maxcv: 1.0167621502852552e-20\n",
       " message: 'Maximum number of function evaluations has been exceeded.'\n",
       "    nfev: 1000\n",
       "  status: 2\n",
       " success: False\n",
-      "       x: array([ 2.19278786e+01,  4.20630237e-01,  2.65766141e-01,  6.52043633e-01,\n",
-      "        7.60078994e-01,  2.74283404e-01, -1.91567916e-19, -1.78015389e-19,\n",
-      "        5.33254602e-01,  5.23867187e-01])\n",
-      "[21.92787859 22.34850883 22.61427497 23.2663186  24.02639759 24.300681\n",
-      " 24.300681   24.300681   24.8339356  25.35780279]\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"
      ]
     },
     {
@@ -630,12 +630,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -683,12 +683,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -706,7 +706,7 @@
     "plt.legend()\n",
     "plt.xlabel(\"rank\")\n",
     "plt.ylabel(\"error\")\n",
-    "plt.title(\"error as a function of sparsity\",fontsize=\"xx-large\")\n",
+    "plt.title(\"error as a function of rank\",fontsize=\"xx-large\")\n",
     "plt.show()\n",
     "plt.close()\n",
     "    "
diff --git a/ErrorAnalysis/ErrorAnalysis_fall.ipynb b/ErrorAnalysis/ErrorAnalysis_fall.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a5f0d7f077ba71602fce652a0b3d1fd0359bf9a4
--- /dev/null
+++ b/ErrorAnalysis/ErrorAnalysis_fall.ipynb
@@ -0,0 +1,367 @@
+{
+ "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": 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": [
+    "#fname=\"LevelCurveData2\"\n",
+    "fname=\"fall_values\"\n",
+    "colorsequence=['b', 'g', 'r', 'c', 'm', 'y', 'k']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   rank   beta   error_year   error_fall   sparsity\n",
+      "0    40      0    41.724601    42.350878   0.838865\n",
+      "1    40   1000    41.843305    42.377424   0.851688\n",
+      "2    40   2000    41.781512    42.350216   0.865042\n",
+      "3    40   3000    41.414115    42.045900   0.872522\n",
+      "4    40   4000    41.120108    41.763680   0.878487\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_raw=pandas.read_csv(fname+\".csv\",na_values=['nan',' nan'])\n",
+    "print(data_raw.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   rank   beta   error_year   error_fall   sparsity\n",
+      "0    40      0    41.724601    42.350878   0.838865\n",
+      "1    40   1000    41.843305    42.377424   0.851688\n",
+      "2    40   2000    41.781512    42.350216   0.865042\n",
+      "3    40   3000    41.414115    42.045900   0.872522\n",
+      "4    40   4000    41.120108    41.763680   0.878487\n",
+      "Index(['rank', 'beta', 'error_year', 'error_fall', 'sparsity'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "data=data_raw.copy()\n",
+    "print(data.head())\n",
+    "data.columns=[colname.strip() for colname in data.columns]\n",
+    "print(data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=data.set_index([\"rank\",\"beta\"],drop=True,append=True)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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\"],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": null,
+   "metadata": {},
+   "outputs": [],
+   "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\"],label=\"N={:}; yearly\".format(rank),color=colorsequence[n])\n",
+    "    plt.plot(beta,df_sorted[\"error_fall\"],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": null,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "plt.legend()\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": null,
+   "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",
+    "        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",
+    "        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": null,
+   "metadata": {},
+   "outputs": [],
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "    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",
+    "        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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sparsityvals=numpy.linspace(start=0.86,stop=0.93,num=5)\n",
+    "print(sparsityvals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "    "
+   ]
+  },
+  {
+   "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/LevelCurveData2.csv b/ErrorAnalysis/LevelCurveData2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7d0f1d7443b6cd477ce4579818d2175985ba1879
--- /dev/null
+++ b/ErrorAnalysis/LevelCurveData2.csv
@@ -0,0 +1,61 @@
+rank,beta,no_iterations,pre_error,post_error,pre_sparsity,post_sparsity,spikey_mean,spikey_std,H_zero_percent
+40,0,263,26.6380314,41.72460105,0.673252845,0.838865433,0.720288853,0.122731287,88.61750652
+40,1000,174,26.95800752,41.84330511,0.693564539,0.851687515,0.664604915,0.112649212,89.37554301
+40,2000,176,26.95295884,41.78151227,0.715965971,0.865041935,0.629207268,0.113267183,90.2856212
+40,3000,177,26.9906727,41.41411522,0.730478776,0.872521978,0.608351187,0.115246254,90.80147698
+40,4000,179,27.03943659,41.12010823,0.741845949,0.878487266,0.594085164,0.117987574,91.2228497
+40,5000,180,27.1076025,40.82374035,0.750373049,0.882699528,0.584893614,0.121282135,91.52476108
+40,6000,179,27.20029113,40.55813278,0.756348969,0.885206338,0.579853049,0.125305767,91.7050391
+40,7000,177,27.31310563,40.60509316,0.760754546,0.887280473,0.577936794,0.130996575,91.86794092
+40,8000,171,27.47106941,40.60657444,0.762338004,0.887846563,0.581200447,0.141840949,91.92224153
+40,9000,182,27.64519409, nan,0.773469573,0.895272986, nan, nan,92.36533449
+50,0,270,25.36197488,41.15354276,0.694136917,0.848008659,0.76900041,0.134002219,90.07558645
+50,1000,187,25.66748831,41.02258413,0.724677771,0.866354897,0.685509061,0.126294919,91.196351
+50,2000,200,25.59166646,40.39731143,0.755849916,0.883902203,0.64143663,0.125233092,92.2502172
+50,3000,211,25.59756563,39.41469432,0.776840097,0.894206458,0.621530973,0.139421922,92.93049522
+50,4000,198,25.72661958,39.49510794,0.781262497,0.896299866,0.624705119,0.163593089,93.07037359
+50,5000,195,25.83122995,39.34477156,0.7885154,0.899773447,0.624036915,0.174155979,93.29539531
+50,6000,187,26.0090278,39.56618099,0.792829846,0.902259747,0.630459369,0.189227622,93.43961772
+50,7000,193,26.131918,39.16171744,0.801339015,0.906129529,0.636479925,0.20342085,93.71937446
+50,8000,179,26.51357444, nan,0.797863492,0.905187923, nan, nan,93.6785404
+50,9000,192,26.57639081, nan,0.807301604,0.910054027, nan, nan,94.01390096
+60,0,283,24.40184251,40.85726627,0.71223134,0.858227836,0.802775151,0.138934537,91.0628439
+60,1000,253,24.34322987,39.46692974,0.774516499,0.893674446,0.674353589,0.141126193,93.33767738
+60,2000,214,24.57246013,39.40797985,0.787484553,0.901314398,0.645567597,0.129150046,93.73515783
+60,3000,207,24.7230114,39.30624734,0.802759261,0.908879227,0.632908232,0.136831449,94.18549088
+60,4000,195,24.90329832,39.10969472,0.810982025,0.912043176,0.642715584,0.165242982,94.42079351
+60,5000,197,25.1288706, nan,0.82076272,0.916585804, nan, nan,94.71763684
+60,6000,194,25.30703604, nan,0.82571777,0.918224164, nan, nan,94.87981465
+60,7000,207,25.42553739, nan,0.836160328,0.923680153, nan, nan,95.23819867
+60,8000,201,25.71551156, nan,0.837919171,0.924444265, nan, nan,95.32652766
+60,9000,213,25.89497942, nan,0.845202193,0.928257341, nan, nan,95.57703446
+70,0,285,23.53705497,40.50742935,0.728575444,0.864803955,0.828064126,0.13663208,92.17202433
+70,1000,195,23.75652443,39.46649386,0.779534856,0.894401055,0.694871202,0.122167592,93.75884324
+70,2000,213,23.69114643,38.15023557,0.814180096,0.9127285,0.657579605,0.146093154,94.80824128
+70,3000,189,23.9887707,38.37024213,0.819441668,0.915190938,0.668286743,0.175769014,94.92987464
+70,4000,203,24.12994877, nan,0.836279361,0.921779327, nan, nan,95.34380042
+70,5000,187,24.38145484, nan,0.835209609,0.921254456, nan, nan,95.344421
+70,6000,171,24.72009516, nan,0.833119359,0.919921666, nan, nan,95.29911878
+70,7000,161,25.05158084, nan,0.831572034,0.919820815, nan, nan,95.31339208
+70,8000,165,25.22024878, nan,0.836619467,0.922003501, nan, nan,95.44991932
+70,9000,157,25.57379936, nan,0.836308845,0.92155345, nan, nan,95.50577138
+80,0,281,22.63627827,39.88715162,0.757383477,0.881275144,0.810585201,0.14156684,93.1782146
+80,1000,205,22.78596345,38.54113083,0.808537679,0.910435722,0.682121992,0.141014856,94.77628149
+80,2000,190,23.08736296, nan,0.824740134,0.919387968, nan, nan,95.24815378
+80,3000,179,23.29840336, nan,0.833686121,0.924051983, nan, nan,95.52074283
+80,4000,178,23.53549908, nan,0.840869179,0.928788821, nan, nan,95.79387489
+80,5000,192,23.70320873, nan,0.853422203,0.933710416, nan, nan,96.13922676
+80,6000,202,23.90587189, nan,0.862361424,0.936971005, nan, nan,96.38086447
+80,7000,171,24.40188262, nan,0.856354554,0.934618934, nan, nan,96.25434405
+80,8000,160,24.81826615, nan,0.854113153,0.934204577, nan, nan,96.26520417
+80,9000,158,25.00547559, nan,0.857617812,0.935699287, nan, nan,96.37977845
+90,0,286,21.94604631,39.84286928,0.764760265,0.885119069,0.841852339,0.137324919,93.70499083
+90,1000,210,22.32535596, nan,0.824693275,0.917812982, nan, nan,95.565209
+90,2000,210,22.62522228, nan,0.849203313,0.929556328, nan, nan,96.20861087
+90,3000,198,23.26554288, nan,0.855818196,0.93281993, nan, nan,96.40457573
+90,4000,206,23.58883198, nan,0.86836241,0.937286512, nan, nan,96.67390675
+90,5000,172,24.02740774, nan,0.863710821,0.935662702, nan, nan,96.57978569
+90,6000,172,24.50401453, nan,0.86758641,0.936817104, nan, nan,96.67438942
+90,7000,162,24.80141684, nan,0.86725454,0.93647063, nan, nan,96.68356019
+90,8000,217,24.84511375, nan,0.885259846,0.942963516, nan, nan,97.07211121
+90,9000,214,25.35023203, nan,0.884124972,0.944176777, nan, nan,97.11941307
diff --git a/ErrorAnalysis/error_by_penalty_fall.png b/ErrorAnalysis/error_by_penalty_fall.png
new file mode 100644
index 0000000000000000000000000000000000000000..8375a1d0ff3d559b00db6603d43bfff76020e3da
Binary files /dev/null and b/ErrorAnalysis/error_by_penalty_fall.png differ
diff --git a/ErrorAnalysis/fall_values.csv b/ErrorAnalysis/fall_values.csv
new file mode 100644
index 0000000000000000000000000000000000000000..508881b82d26b6e587c4c1b5fcafd747c11cd429
--- /dev/null
+++ b/ErrorAnalysis/fall_values.csv
@@ -0,0 +1,121 @@
+rank, beta, error_year, error_fall, sparsity
+40,0,41.72460105025903,42.35087767646531,0.8388654327627628
+
+40,1000,41.84330510906969,42.37742447547956,0.8516875149543234
+
+40,2000,41.78151227240913,42.35021628422066,0.8650419351690943
+
+40,3000,41.41411522079264,42.045899510275135,0.8725219776895965
+
+40,4000,41.12010822994588,41.76367991017106,0.8784872660775023
+
+40,5000,40.82374034545264,41.484024386030626,0.8826995277177598
+
+40,6000,40.55813277967879,41.217559656362475,0.8852063376852094
+
+40,7000,40.605093156076876,41.273221222968274,0.8872804729674099
+
+40,8000,40.60657444051252,41.26086011612543,0.8878465633146483
+
+40,9000,nan,nan,nan
+
+50,0,41.15354276346608,41.70469689644759,0.8480086590097954
+
+50,1000,41.022584127903315,41.57787503004292,0.8663548974850813
+
+50,2000,40.397311433616174,41.00388585671356,0.8839022026959876
+
+50,3000,39.41469431687864,40.00149718654941,0.8942064579237392
+
+50,4000,39.4951079369519,40.043444794793324,0.8962998659218735
+
+50,5000,39.34477156457859,39.87379132052321,0.8997734472491155
+
+50,6000,39.5661809934229,40.11896452288769,0.9022597468179229
+
+50,7000,39.16171743763571,39.719294567165555,0.9061295288417061
+
+50,8000,nan,nan,nan
+
+50,9000,nan,nan,nan
+
+60,0,40.85726627482434,41.39834759215941,0.8582278355670888
+
+60,1000,39.466929735758285,40.078087284608856,0.8936744459824406
+
+60,2000,39.40797984713058,40.04281502999035,0.9013143984785661
+
+60,3000,39.30624734316177,39.93749229393429,0.9088792267677279
+
+60,4000,39.10969471780593,39.7040229909436,0.9120431759673147
+
+60,5000,nan,nan,nan
+
+60,6000,nan,nan,nan
+
+60,7000,nan,nan,nan
+
+60,8000,nan,nan,nan
+
+60,9000,nan,nan,nan
+
+70,0,40.507429353321555,41.06526915889187,0.8648039548726123
+
+70,1000,39.46649385660883,39.98316942181214,0.8944010552242151
+
+70,2000,38.150235566876155,38.68606994809502,0.9127285001068114
+
+70,3000,38.3702421276659,38.9018550948417,0.9151909375374807
+
+70,4000,nan,nan,nan
+
+70,5000,nan,nan,nan
+
+70,6000,nan,nan,nan
+
+70,7000,nan,nan,nan
+
+70,8000,nan,nan,nan
+
+70,9000,nan,nan,nan
+
+80,0,39.8871516166041,40.3883833428427,0.8812751435156017
+
+80,1000,38.54113083135668,38.98303687191838,0.9104357215404325
+
+80,2000,nan,nan,nan
+
+80,3000,nan,nan,nan
+
+80,4000,nan,nan,nan
+
+80,5000,nan,nan,nan
+
+80,6000,nan,nan,nan
+
+80,7000,nan,nan,nan
+
+80,8000,nan,nan,nan
+
+80,9000,nan,nan,nan
+
+90,0,39.84286928349262,40.39305661122891,0.8851190690740084
+
+90,1000,nan,nan,nan
+
+90,2000,nan,nan,nan
+
+90,3000,nan,nan,nan
+
+90,4000,nan,nan,nan
+
+90,5000,nan,nan,nan
+
+90,6000,nan,nan,nan
+
+90,7000,nan,nan,nan
+
+90,8000,nan,nan,nan
+
+90,9000,nan,nan,nan
+
diff --git a/ErrorAnalysis/sparsity_by_penalty_fall.png b/ErrorAnalysis/sparsity_by_penalty_fall.png
new file mode 100644
index 0000000000000000000000000000000000000000..8d0dbf182a718f47fdcbfa3b2d65f72c399d4efb
Binary files /dev/null and b/ErrorAnalysis/sparsity_by_penalty_fall.png differ