diff --git a/src/explore.ipynb b/src/explore.ipynb
index ab19810015cf7980600792b05f0faafb145e2156..69b1cc9252618c99cc31b2f1bcee31d2c9a98333 100644
--- a/src/explore.ipynb
+++ b/src/explore.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -211,12 +211,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -236,13 +236,72 @@
     "\n",
     "fig1, ax1 = plt.subplots()\n",
     "ax1.set_prop_cycle(color=['lightskyblue', 'steelblue', 'yellowgreen', 'olivedrab', 'lightgrey', 'grey'])\n",
-    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
+    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', explode=(0, 0, 0.1, 0.1, 0, 0), startangle=90)\n",
     "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
     "\n",
     "\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+7rYSONrIqLj/bi7WhJm1nSaktF9j0glDATgIIc8DmARgJKU038dFZPzQeVcbLi4uRq9evc762IsvvojVq1ef8fv169djwoQJZ31OXFwcqqu9PwikJedtyTFvvPFGq2N/9dVXeOSRR1r9PD9HbC7xf5lH6lP/vWxvm0gmE/tEY+mDA8Rtz43A42O6UTel+K2wkszbcwy/7a/iCyqtqHN44O0/JcSg4Urq/9rfpcrmwbYjDfhy13H+378cIK+uLsKWI43c1SnR8rqnh2PplAHS3YNioOH8f7fet1fu120pqkm0ucQf0HzzSQAMZMmEOZeLrqG88sor3iyHX3jjjTfw3HPP+boYPucSpWeqmlwTnl221+jPrVyhRg2eHNudjkyIgEwpDlTZuN/zy9HgFFW7Wlt0Aneg6tyL99Y5RGw6XEc2Ha4jwXoBaR0s3E39Y+iDI7qSfWWN8tsrD3AF5YrvfntRKIAXvss3fHx76uDkDkFfU0pvh5dm1DOB6YL7oUiShMmTJyM5ORljx46Fw9F8N3bPPfdgyZIlAICVK1ciISEB6enpWLbsr60OampqMHbsWCQnJ2PSpEk4ub9m3rx5yMjIQFpaGh544AFIUvPe4mazGc8//zxSU1MxcOBAVFRUnFGmiznvyc52zLPPPguHw4G0tDTccccd5z3XnDlz0KNHD2RkZGDLli0XfpXblgkujzz9oflZJn+dtJgUbcbc+/pJvz4+BGmxwfKGQ9Xkm+xS8ntpA9fgVG8UlpbnwBGCsqaWrXvY4BSx4VAdmbH2MHln/WE4ZMhz7uuLZVMGSIO7tlO4tBdHlCkeX5xjPFbnuNbult7zdXkY/3bBhHLgwAE8/PDDyMvLQ0hICJYuXXrK406nE5MnT8aPP/6IzMxMlJeXn3js5ZdfxtChQ5GXl4eJEyeipKQEAFBQUIDFixdjy5YtyMrKAs/zmD9/PgDAZrNh4MCByM7OxvDhwzF79uwzynQx5/3TuY558803YTAYkJWVhfnz55/zuLKyMkyfPh1btmzB5s2bkZ8fULX/ZIdHWvTYwmxDuR8up9IzyoxlUwaIc+7tB04Avssrw6/7q/gyH5U1WC/AJckXlXXLm9yYt6dMmLbyIPZW2sjbN/XGT48Nlod2C/N2MS+Z0yPjga9/N9XZ3JNFSb7f1+Vh/NcFm7zi4+ORlpYGAOjbty+Ki4tPeXzfvn2Ij49H9+7dAQB33nknZs2aBQDYuHHjiRrL+PHjERoaCgBYs2YNMjMz0b9/fwCAw+FAZGQkAECr1Z7og+nbty9WrVp1Rpku5rx/askx5ztux44dGDFiBCIiIgAAt9xyC/bv33/W144QMgDAS3/8KAP4DsBcP13K22J3ib+89et+Y87xRl+X5RShRg3evqmXnNopmCusspJvc0rhEuWL6lT3piC9BvbmGZ4XvfOpzS1hZWENt2p/DYbGh5K3buqFqiaX9NIPBXyWH+0p0+gU8ejCbNP8Sf3fF3hsRCtm0zOXjwsmFJ1Od+L/8zx/osnrUlBKcffdd2PGjBlnPKbRaEAIORFPFFvehHG+87bmmPMd9/3337e4PAAKAHz4x/83AbgfwDRCyFsAvvSjmcXE6hLnrd1XFfFjdrlf9RY/MaYbbunfCUfq7FiaWwa7R/J5IvlTsF6gtTa3V8ojUWDDoTqyo6QBV/UMw2d39kFBeaP0xKJcvs7hH/vLFNfY8f7qg/qpo7t+b9IJKQD88caI8aFL3lM+ISEBxcXFKCoqAgAsXPjXitXDhw/HggULAAC//PIL6urqAACjR4/GkiVLUFlZCQCora3FkSNHWhzzUs57vmM0Gg08Hs95jxswYAA2bNiAmpoaeDwefPvtt+csJ6W0kVL6yx//llBKxwK4BcB4AAcJIVMJIcYW/+EK8UjyI9VNrtEzfi7U+7osfxoQH4q1Tw6Vr02Npr/sq8Smw7Wc3XNmf5gvhRm1cmmj26sJ2CnK+D6vin91dRFkwmHF1MH0roEx3gxxSZZkHudyjjXEONzSm74uC+N/Ljmh6PV6zJo1C+PHj0d6evopzUfTp0/Hxo0bkZycjGXLliE2tnkCflJSEl577TWMHTsWKSkpGDNmDMrKyloc81LOe75j7r//fqSkpOCOO+4453HR0dF46aWXMGjQIAwZMgSJiYmter0opdsppeMBXA9gBIAiQsjThBBzq07kPRluUX5r6qIckz8s9sgT4OPbUuUPb0vFgRobvttbRmrs/nkjHKwXyOHaS6+xn02dQ8QXO4/z/9tdSu6/Ih7fPpAhtTNpFInVWs9/n290S/IDaP78MswJbKa8Qlo6U54QkgLgeQAjAXwA4GNKqVqN53q7Wyya/kNBhzUFrV613Oviwgz44p6+sihTrC+q5qxu/6qRnO4ffWMwbeUBNLqULadRw+HWPtFSYqSJe3/VAbIk84zVUFQ3uGs7vHNT7xqDlu8OoM7X5WH8wyXXUJhLQynNoZTeAuAKAAlorrG8TAhRfByp0yM9t+dIfYg/JJOb0jtg8QMDUFLvwE8FFX6fTExaHjKliicTALB7ZHy58zj/dWYp+deY7njv7718XpXcWlSLH3PKzFaXOBfNEx4ZhiUUf0EpLaCU3gVgIIBOAA4QQmYQQiIUCtmVUjz1xs+FPu/D+eCWFPrkuO5Ye7Aae443cG1h5lyQToBLlFXNejllVry59jCSOgaTZVMGSEaNb8cnvPfbQV2dzT1SkundPi0I4zdYQvEzlNKDlNJ/AugLIARAISHkXUJItBfDEKtTnDN702GtL+ebmHU8Vjw6SOrVKQjf7S1HaaO/DHq7sGC9BlaXqHruq7F78Obaw6TJI5Ofpg6mncN8dz/glmQ8+W2uyS3KHwNgzeQMSyj+ilJaTCmdAiAFzcO78wghHxFCOnnh9Dc2ODzp87Yf9dktbrhZixWPDpYpAVYUVBCHn43gupAQg0aqtHl8sriqW6L4fPsxbvPhOrpwcn8M6hLqi2IAAA5W2vDZhsN6q5M1fTFnmYditVpLkpKSfFGWgGK1Wku8cR5K6TEAUwkhMwA8CSCHEPINgDcppcUXcUqLwy3Nmv5DgclXy9F3DjNg/qT+9FiDk24truXbQhPX6UKNGvx+3LdrcP1SWMNV2Tz0vVtSyWsrCvBT7pnLFKlh4c6j/K0ZnVLNeuEaNG+6xVymzkgoAbIpVMChlJYDePqPSZH/ApBJCFkO4A1K6cGWnsfull7buL/asKek/sIHKyC2nQELJmfQgsomml3a6DeTFFsrSKfhDtUoM2S4NXYfaySiTOkLExKJlufwXVbLh997iyhTzPi50DTjxuTPTDqhCwD/mInJqI41ebUxlNJqSunzALoBOAJgGyFkHiGkJRNielFKJ7/z636DsqU8u5hQAxZOzqD7mpNJm/3scQTQazhysNrm66IAALJKm8icXcfxzNU9Mb53lE/KsPlgDQorrKGiLE/xSQEYv9Bmv9SXO0ppHaX0ZTR3huYBWE8I+eaPeS1nw1md4twP1hTp6uzq30DGtDNg0f0Z9ECNlWa14WQCABadAI8kU39ajHlvuRX/3XEML0xIxLDuvllgcsbPhSZRoq8D8F2nDuNTbfqLzZxY3mUGgC4AdgD4lRDyPSGk72mH3lTR6Oy+bM9x1d/zIJ2ABZP6y4dqbXLmsYY2/5kL1mvg9FzcKsNKKqi0YW5mKX3rpl5IjFZ/4YWiKhtWF1TyTo/0f6oHZ/xCm/9yM80opTZK6btoTixrAHxPCPmZENILAGlyiq9+tLbI7It++IUPZEjVdjfdebS+zfaZnCxIL6i6iVdrZJc2kRV5VXTWP9JplEWrevz/rD9sAPAogHDVgzM+xxJKgKGUOiilH6G5j2U5mocdX2FziR03HahRvTz/uSNNMmg5bsOhmoBIJgAQatBIFU1uv/3urD9UR/Ycb5LnT86QdLy6xSxrcOLn3HLO4ZaeVzUw4xf89kvBXBpKqYtS+jmldEGj0/PS7E3FRrUrJw9eEY+02GD+18IqIvnzXsKtFGrQkpP3kfdH32aX87UOkXx2V5rqTXOfbzysJwQPAGivdmzGt1hCCXxJBCTjpxx19zlJ6RiEe4d0xm/7q+Bvy85fKotO4A5Wn3sfeX8gU2D2jmNctygLd3uGN+bCtlxVkxvLs8o4u1t6QdXAjM+xhBLgrC7xuQU7jmqaNxZUh04g+PiONDmntFGutvnn0vMXS8MT8BzB0Qb/2yL5dDa3hC93HsMjo7qiY4i6W93M3Vai4wnuBeCTIeqMb/hk6QhGNe0Fjvxt8a5jqr7PH9yaKjk8EnLL/XviIs8RhOgFhBi0aGfUiGFGrT3UoOE1PKcjBIQ033ARQggopXBL1O4SJUgyNVzZvR2ON7jI0Xon/Hll5MIqO7YeqZdn/SMd4z/cqtoNZFmDE/llTVKf2JCJABaoFZfxLZZQApjTI039Obcc9SpuIXtFjzD0iQ3hv9tbBn/sNRE4gtgQA7qFm5raW/Q6UaZlAM3R8txOQkgumuf0lAGQ/vgnA5AJIbxOIB10AhcLIOOahIh4tyQP1Alcclmjy7PtSIMlq7SRNKmwnH1rLd9bySWN6kJfvDaBvvLjPtWaPhftOmbpGmF6PMigYQnlMnHGBltMwDA5PVL5LbN2mo8qtKvg6TgCrHtquJxV2kAO1tj8ZlgtR4DYECO6h5us0UF6jSjLO3QC/wWaR8Fd6mZmOgDjHB7pXoEjVx1rcHm2H6m3ZJU2weZHNZcoixb/NyIe987JREG5OmuQaXiCtU8Oc5p0Qi8ARaoEZXyK9aEEKEmm9+4uriNqJRMA+PfVPeGWZL9JJhwBekaY6d9TO9oHx7Xb3SnE8BjPkWidwF8BYC4uPZkAgAvADwYNP1HDc+3i2xnuuT458qdXxnVzXNUzXNTwfvFSoKLJjS3F9dLbN/cS1YrpkSh+zC4jLlGarFZMxrdYQglQdrf0xP+2lZjUitc+SIfr0qKxubjGL66gcaFG3Jza0davU8gWg4YfoxO4/gDmQNntah0Alhm1/ASdwPUe3b3dr69d1d0+MDaY+sOL8lNBFR9q1Ap/S++gWsyle0p1soz7wZrXLwssoQSmLgRo/7uKKwp/cGuKdLjWLtX6YJ2wk1l0Aq5Nam8bEt/ugFHDX6sVuGEAtvqgKEUGDT/BpOVH/y0lKvvFMV1tCZGq5fezcooyluRU0MdGd5PV+uIXVdlwvN4hABinUkjGh1hCCUCSTG9Yv78Kas0lHNItDHHhJn63j5dW6RxqoNcnt3eEGjTTtDyXCGCdL8vzh+0GDZ8eYdbeNXlAp4qbUqJcnA+rK7uONpAGp4j/u6aHajEX7jxmaXR6HlMtIOMzLKEEIKtLvGtVfqVq4/+fvbqHlFvWKLtUnOtyMp4AgzqHOofFh1VoeG44z5H30TxCy19QAN/pBC5pYGzwrseGdrYZNb756lEAS3MruPG9o6lGpcz2W14FtDw3HIBv1tZnVMMSSuAJ1wtc0s7DSnYV/CUtJgiRFh2fX9Hkk8+SWcvjuuRoW5cw01pNc61kty/K0UK1eg0/MiZE/8Xzo7vYoy06nxTiQLUd9Q4PfWJsN1XqsDa3hLX7qqgk07vUiMf4DksogWfCruI6t0tUp7bw72sS5LyKJurxwVpdJi2PCUnt7Rad8IqW5yYA8M02lK0j6gRuqlknPPDUiDh7ig+WmQeAH/OruAmp0VCr+e23/EqD1SnerE40xldYQgkwjQ7Pnb/mVapylYoJNSA+zMjllzep3itg0PAYnxhl1/LcdJ4jbwN+OY/ynHiOzNMJ3PC7+3Vs8EVS2VtuhcMj494hnVWJt+dIPYxaPg2A+mvqM6phCSWwGPUafuimA9WqBHtqXHd6uM6uet+JXuAwITHKrhO4GTxHZqoa3LsydQI36u5+HW3dwoyqBqYAVuRXkb/366TKm2d1iTje4HQCyFAjHuMbLKEEljH7K5pcjU7l564JhGBAfDvsq7Sq+hnS8gTjE6NseoF7X+C419SMrZA9OoG79sFBnRxRZnVv3rNKmxBk0HA9otSpIW05UG0QJXmUKsEYn2AJJYBYneJtP+dWWNSIdd/QznCKElV7NeGh8WEOg4ZfLPDcNFUDK2udhucefXhIrN3fbZbxAAAgAElEQVSg4ugvUabYc6xBfmRkF1WaC3cW12lsbulaNWIxvsESSuAgAk+u2bC/WpX+jOv7REv5Fer2nXRpZ6QdgvSVGp57BG2sz+RCeI58YdTy8yZldFJ1o5UtxfVceudQVWL9XlIPg4ZPQfP6Z0wAYgklcHR2izJf1uBUPJBZxyPSoueLauyqJRSTlsfguHZODc/dgOYlTgKOXuAe6RyqP963Y5BqMYvrnHBJMm5Ii1Y8ltUl4Xi9wwlggOLBGJ9gCSVw9Csob1Jl4b/bMmJQY3fLam3aRQCM7BpuI4S8AiBLlaC+4dFr+PtuTo2ya1VcVHLzoTp6x8AYVSaCbj5QY/CwfpSAxRJKgHCLckZWSYMqvatX9YqSDqm4onDPSLMcbNDsEzjyjloxfWizwJGVVyeEq9Y5lVtu5TqEGFS5FuwsrtPYXNIENWIx6mMJJUDY3eIVeaWNir+fHAE6hRr4o/UOVRKKwBGkdwxxaXluMvxrORXF6DX8o1d0aSeGmzSqxCttdIFSkD6xwYrHyiqph0nH9wag7p7EjCpYQgkMxKDhkwvKlN84aXzv9nB6ZKrWtreJURaJAGsB/K5KQP9QyhG8fmtatE2tgPsqrdLENOWXtbe5JVQ0upwAkhUPxqiOJZTA0F6UKV+jwhDeq3u3l481OFTpPOEJkNI+yK0VuGfViOdPBJ57N76dwdEpWJ0b+ZwyK9+nc4gqdwnH6hwAoM4UfUZVLKEEhsSjtQ6XGoF6RJnlCqtLlWXqu4abKQV2AdirRjw/4+IIPh8cF6zK+7qv0oZIi45XY22vIzV2PYA45SMxamMJJTAkFFY0qTLN2qIXhCqrKtc49G5vseoE7hVVgvkhDc/NyYgJpmoM+LK6JTg8Mvp1DlE81tFau9buFnsqHohRHUsoAcDuFtMOVtoU3/8kpVMwZNp88VGaWcvDqOUBYL3iwfxXkUxRmNRenaVRShud4ujESOXjNDjhFmWWUAIQSygBwC3SPoerle+/HdkzHDV2dXrjY0IMVJbxEy6TkV3nYtTyHw+NC7WqEetwrYNPjLYo/nqX1jvBERKndBxGfSyhBACeIx1L65WfId8jykxr7W5Vhgt3CTM1aQVusRqx/Ny3PSKMgkmrfLfV8QYXibDoFF/SprTeCb2Gj0LznFUmgLCEEgAEjpisLuUnyce0M0oNTo/inxktTxBm1OoArFI6VhvQ4JbkzC5hyu/ofLzBCYteo3jmsrpEyJRyAJTvsGFUxRJKABB4YlAjoYQYNaRJhaXxOwTpIcryDgCqzcPwZ1qe29oxSK/4UO1qmwd6DUcEFYZ6VTW5nGAjvQIOSyhtn8ATIjg9yk8N0Qkc36hC4go1aGUNz21QPFAboeG53+Pa6RVPrhSAS5TRMVT5uS9/NNHGKR6IURVLKG2fxS3Jis9oJGheBkWNPvkQg8bOEXJQ8UBtR45aExxtbon2iFR+VNmRGrsOLKEEHJZQ2r4gh0dSvNoQYtRAolSVTUhCDBoJAEsof9lv0Ql6jQoTUpqcohwfYVI8ToPDowWgymZwjHpYQmn7ghxuSfFqQ6RFB1FSZ08rk5bXAShSJVjb4HGK8tFoi/L7UtU7RdopRPkBAB6ZEkmmbKOtAMMSStsXZHMpf6UPt2jhkWTF42h5Ao4QAqBS6VhtCaX0cDuj8qsPNzhFzqIXFI/jkWSwhBJ4WEJp+4LUGOEVZtLBLVPFE4pO4CFR2oAA2+LXC+xqjL7ySDIReOUvC6JEIclU+aoQoyrlb0UYpQU1OUXFrzRmHQ9Z+QoKKKUggCqLT7YlHCGelGgLQhSuPcSG6ImR5/CPQbGKxkmLCQallPWhBBiWUNo+i9UlKn4B1vAcOEIUzyh/5Cz2uTyNwEshPaKIHBvGKfoeGDUcD8rRK9Og6Dj0MD04cGIEe6sDC3s32z6PwCl/oXd4JKixtLlMKQhhNZTTUcgNWdWZ3KEmZQe/9QnvT61OSh5e+7qi78HtCdfgodTbipWMwaiP9aG0fXWhRq3io7waHR5wHFH88yJTCgLCEsppKGCSqPJzgDhwVJSVjyNwAjjCqbMPAqMallDavtoQFUb/1Nk9qtRQJAoQAg3YwoGns4hU+cEXWk5DXZLyO39qOAECxyu/oimjKpZQ2r5ai15Q/H0sa3BCq8J+fpJMIclUBBCldKy2hCd8zwZ3veJxTBozrXYqH0fgeAgcz2ooAYYllLav1qwXFO8LK613QsNzqlQbmlyiCwDbgOkvkQTEZPU0KR7IIBi54objisfR8loZgPJVIUZVLKG0fXUGDa/4BDGxueYAnfKVIdTaPRoAyYoHajvS61y1qjQPGXgDV1hbrHicDqYIB4AyxQMxqmIJpe1zyZRKBuW3sYAoU9mowkZPlVaX0S3KwxQP1EZQSvtWOSuMasTS8jrsry9WPE6sJVoEcFjxQIyqWEIJAB5JtgYblB8B7vJI1KhC4qq2uQBgkOKB2gi37Lqi2lml+MgLLaeDTGU4ROW7NqJN4QKAYsUDMapiCSUAiBJtCDIoP9KrrMFFQlSIU+vwgBBEAVB2unYbwREurcZZrXicIG0QnKJL8Y11BE6ARWvSAziqdCxGXSyhBACZ0rpgFS70BWWNXJgKc14oBYrr7FSm9FalY7UB3SiFpcnTqHigMF04apz1ik+SbW8Mg1Ny1wFQfhw0oyqWUAJDdahJ+YSy41AtwkxaVT4zRdU2gyjR+9WI5c8kWbqvqPGAKnNywvWRYnFDqeLvbwdzBDySh9VOAhBLKAHArBN29Ig0K15z2HywBmatQNSY4Fje5AIFOgBIVD6a3+JkyJMPNhaqssx7hCGS21qarfi728EcCY7jDigdh1EfSygBQOC53akxwYrvOe7wyHCJktzOqFU6FCiAohobL8n0TsWD+a8xDtGuq3PVKh6IIxxMGjP365HNisfqaIqSzRpjvuKBGNWxhBIY9vSMsijf5gXgWJ2DRlv0quxVsr/KqpUpfQiAKkNm/Y1bcr2YW5ulyhLvIdpQOEUXbXQrfl+CuOCOdo5wbMhwAGIJJTAcE3gihZuVrznsOlzHx4YalF89EECdw4OyRqdWkukTasTzM70p0Odwkzo7IXcwxdBDDcdUiRVjaS+BzUEJSCyhBAbq9Eh5idHK38x+k3kMYUatwKu0dOOuo/VGCvosgDB1IvoHt+R+K78uVytTxUfxAgDiLV3or8VbVXlXO5ojdGAJJSCxhBIgjFphY2K0RfGrz9FaJxweSY4wq7MdeKNLRFGNnfdI8kuqBPQPN7ll9/C8ulxVlvHX83oEaYK57w6uUTxW56AOICA2AMovGMaojiWUAKEVuN2pnYKtasTKK20kcaFGVZq9AOD3Y/V6AvwTQJxaMX0oXJTF/24qW2uSVdj/BAA6mmLQ4GoUHaLyy4X1j+oFicpr0DzuggkwLKEEjj0J0RZV7mi/3lZCuoaZ1Gr1gkOUkV3WqHFL8lIAqgw+8BW35J51oKFQX+WsVC1mZ3MXcVuZOrWhoR3TrRataYUasRj1sYQSOA6ZtAKvxtIoW4tqIcqy3D5InWYvAMgpaxRqbO4EjyS/o1pQ9V0nUs+4PdU7VXthBSKgvTFamJX7reL3BwQE/aKSBADrlI7F+AZLKIFDtrulfQkqdMwDwO4j9aR7uPKTKU+2rqjaKMp0MoDr1Iyrknai7PlqU9k6oxpb/f6psyUetc56+WhTueKxuoXEQqa0DoA6w8kY1bGEEkAMWm5V/7gQVdZHmr3hMIkNMfCCGtPm/+ASZaw5UGUUJXkegHjVAivP6Jbcq/Y37DNWOJS/sJ8sMSSZLtr3qyrXgYz2vSgh5Dc1YjG+wRJKANEJ/Hdjk6IcasTKK2tCg8MjdQs3qdq5WmVzI/N4g9EjyWsBRKoZWyEaj+ReUWo/lri7aod6bYhonsxo1gSReQU/qhJvWKe+VpPG8LMqwRifYAklsOwMM2vRIUSvSrBZGw7zKdFBqsQ6WX5FE59f0dTRI8nbAESoXgDvEdySe51Tdg3fVLbOoHbwpNDe0q7yPCqq0MTGEw4p4T10ANYrHozxGZZQAoskynTF8O7hqtQaluwpBaWgMcGqXwux53iDRva44qjbXoC2uW+Kzik6vy+1lfXRclouLayfOjMY/6DldIizdOHf3f2VKm2WCe26wCOLZQDUG77GqI4llABj1gmLr+oV1aRWvCWZx7nUDkGqzymYmBAmaRuOgu6YG0I9jj0AeqtdhksQ6RSd24saD436bO/nxg+yPiI9gnuSvhEDVOuN79UuRSqzVYnFTaWqxMto30sWCM+auwIcSyiBZ3XP9hatGsOHAeDTdYdg0QmItqjX/D+hZzsp2F3N0W8f47BrPk9/e6sd9Ti3UVmaDEC9UQIXZ4BLcudtKdua9L+CuQaJSqhyVuH97A9JV0t3rn/EIMWTioE3ICEkiZ++9RPl943+w/guV9gMGv1yteIxvsESSuCxuUXptyuTIlSpNYgyxbI9pWRAbKgqTTbjurWTwmkTRxc/TPDnyriFawhd+IAJtSXvU5dtK/xzBJjGI3umuyTXuoX7F4avLPlVS0+aLF7jrMH7WR+QeEtXMiBysKJJJTWsr1TcUCpmVRUqGeaEnqFxaG8MdwFYrUpAxmdYQglAFr3mvxP7dFCt2ev9VQdg0PCkc6iyfSmjuoTK0RoHRxdNIXA2nPpg9SHQr+810Z1f96ce514qiY/Dfz7fg10ex5EyW/m/Z+55z5BXe/atQGpdtXj39/e5WHM8GRg5VJGkYtZY0CWoG//c5g9Vq53c3GOcU+D4TwGoOm+JUZ+/fOEY7/o1PtzEqzWTXaLA7I2HyYDYUEoUanAaGhdCYw0iRxdOIbDVnP0gKgG75vN03n1GVBe9Rl22PQAGKVOiFulBPc7F1G1bpakraW/g9dpG9/n3hq931+Pd39/nOpljyeCoYV6/AKeH95dyqvZLBxtKvH3qs9LzOlwdPwxaXjNblYCMT7GEEpjcokyXjUuOUm3k0JytJbC7Jdo7KsjrMQfEhNBuFhC66CGgqeLCT6g7Cjp/solu/DSF2mpXU5c1C8D1ANRYr4oAuJK67euox5mNQ+sn0l9fNGLLRySI19GhHYZe8PVpcDdg5p53uQ7GGDIk6gqvJZVIQxQ6GmP4Zza+r8q6XQAwtvMgiLK4HWx2/GWBJZQAZdYJs2/p38muZg/144uyuZQOQVyQznutKekdg5EYyhP6zaNAfWuuSRTI/YHQWTca6ap3Umn1oa+p21FJRdcMAF28VsC/RFNKH6QexyFqq/6O7v3uCvrzv/U0f4UGogsAoNn1FTcudgwXqgu94MmaPFa8s+ddrr2xAxnWfqRMLnGsAU94DGs/ii4s/AW1rvpLOldr3JE4ocmiNb2nWkDGpwilbBXpAEWsTjFv+g/5iesKq1UL+sbEJNo3LhQrCiouOZf1bm9B3ygd6LePARVe6EAO7wKScp0LiVdRENIEWdxO9EGrAWwDkA3A3YqzBQMYRCXPaEie68AJnVFZINJDG004T2e3PPhh+Yhei8/2zmrRzZxRMOKZ9KfkKmcF3VS2jqcXuep7/4hBUqS+A8YsvV+12km3kFj876rX6/Ky9kb279//EQCrKaV71YrPqI8llMA2sajSOvfmz3ea1QrIEWDd08Pk3LImUlhlveik0jPCjEEdDaDLngKO53iziM1CY4DoXiCdUh3olOaBJVIPt+MAQEvACZUQ9JTwfBkAG6WUh+jsAlnqCsLFgNdEAkRA43EHKvJNtGo/j5oioCW7K3JaeK55nX57aBnJrm7Z32UUjHi6z5Nyrauabihb0+qkEq6PwJhO1+DWFc/gcKN6LU/PZkxyXd9l5Ht6je55AM8CeATAIQD/AbCUUupSrTCMKlhCCWyczSUWP/FNbsyu4jrVgg7tFoZ3/94bP+aXo8HZ+rUqu7QzYnisGfSH54AjuxQo4VloDEBUT8AUBugtIIPuk1FXzMFaKQMAddRzcNSh+V89TgxZvhhxg+HqPREzdr8Fh9Sypdf0gh7P9HlKrnfX0vWlq1ucVDjC44a4m7Bo32/4NHvRxZe5lXS8Fmtu+sJp1OgTABwBAEKIBsC1AKYASAHwFYDPKaWHVCsYoyjWhxLYZJNOeHHKiHhVdnL80+aDNVhTUCmP6RFBW7sacUyIoTmZ/PyyeskEADwO4FgWULgGyP4ekEVKS3aC5v/I0fwfORzeBJTvBRqOX1oyAYDirRBsNdJ1Xa5tcYe7U3Ti7cyZXLA2lIzsOFbmWvjVHRw1TK52NMpqJhMAuDJ2IERZ3I0/kgkAUEo9lNJllNIxAIYBEADsJIT8Qgi5jhCiWnMcowyWUALfgh5RFleiSvuk/Om57/K5OptHHhrfrsWjvjpYdBgdZwH97S2Kos1KFu/COB6QWtOl0jr81s/4lLBefHxQy+dgOmUn3sp8h7MIQXRUx7ESR87/9U0ISZKjjR3J3SufV/V7TkDwz143WoN05pnnOoZSup9S+iSAGAALAfwbwGFCyDRCSLRaZWW8izV5XQZEmT6+9WDNa48vzjGpGdes47Hy8SE0q7QRF+pPiTBpMb57MOi6jyj2/ujz5VPIlBUy3fkFh1oFW2OSr0dj5/70zcy3SWs21dJyWjyd/qTkkOxkzfFfubPtPR9liMaojmPx0OrXkVmZ581SX9Do2IGYPnBKoVlrTEQr9o4nhKShuTns72ieVf8fAOuoly5SsbGxa81mc1tcSNRvWa3WkpKSklF//qzabFnGdwSOzM6ID32lc5gRR2rsqsW1uiRMmZdF/nt3OqxuEccbnGc9LtQg4JpuwZRu+YJi74/+UWvmOEVrKACAvOUwxPSTR8eMIr+VrGrx3+2W3Xhrzzv8M32ekq7seJW85vhK7uSEZBLMGNlhDL7KW04zK/NUTc484fBE33/YzFrjVLQimQAApTQLwAOEkKcB3AngAwBaQshnAL6izbs9XjSz2Rybn5/f9VLOwZwqKSnplJ/948vLKM3GEfL+pKFxqmy+dbLc4414dcU+jOwajnCT9ozHLVoB1/UIpdi9UMaexf7zeSQ8geRRPIxmx3/5KzoM5yIMrdvWRZRFvJ05k9dyOnplx6tl/o/uBw2nwZhOV9ONx/bQ/2QvVr2md038cGrWGPcBuOidGSmljZTST9Hccf9PAP3Q3Bw2hxCSQYhS6zEwl8p/vsCMorQC98GViRGIUmk5lpOtyCnH/O1H6dgekbCcNOnRqOEwMSFERs5yGdvn+FeHLOGI4jUUAKg7Ar5sL729x62tnrwoojmpCERDx3a6RtbzeoztNJ4WN5TTZza9q/pFV8MJmJp+p8OiNbW6dnI2tNlmSukdALoDKACwCEAmIWQyIUTVJlzmwlhCuXzUysCHz1zVQ702r5N8uLaIrNtXKV+TEAWTlode4PC3hHYyKVxNsfET/0omAEA4qFFDAQCy6ysSoQ1F/6h+rb4IS5Dw9p6ZPMDRG+NvRa3DRu/9dZpPvte3JVwjaThhB4At3j43pbSKUvo2gG4AngMwAUAJIeQjQkhya89XXFyMXr16nfWxF198EatXn7kw8vr16zFhwoSzPicuLg7V1d6fQHzyeQcPHnzJ5zjZSy+9hJkzzzlu4qzM5vNPaWMJ5TJi0PAvD4gPrR/aLcwn8Z/7Lp/bdrBGvjaxPf6W2E7mDm+hWD3T/5IJ0NyHIquTUAAZmt8XctfFXUssmtbPQeU5HjaPnVTaa+Tbfn6Gu9jZ9JeinT4Y9/e+2W3Rmh5UMg6lVKaUrqSUXg+gD4A6AKsIIRsIIbcRQi65Cv7KK6/gyiuvvOSyetvWrVt9XYQLYgnl8uIwaoV/vHRdot2g8c11/IXl+Rwne6hGq+Wwaz7vhZYRZRBetRoKAKA0C1zDcenGrhNbtRikjtdiSq8HZZ5o6fXLH+PklszWV8C/0u9yEIL/AtivVkxKaQml9EUAnQF8BGASmmstMwghFxyPLUkSJk+ejOTkZIwdOxYOR3MX4z333IMlS5YAAFauXImEhASkp6dj2bJlJ55bU1ODsWPHIjk5GZMmTcLJA9HmzZuHjIwMpKWl4YEHHoAkNb+lZrMZzz//PFJTUzFw4EBUVJy50On5zvtn7aCsrAzDhw9HWloaevXqhU2bNgEApkyZgn79+iE5ORnTp08/5bxvv/02evfujYyMDBw8ePCMuEVFRbjqqqvQt29fDBs2DPv27QMAHD58GIMGDULv3r3xwgsvXOglZQnlMrRGJ3ArHhnVRfVlLwQOWH5/moQDO6h9zVxKbvkEiG51a4XyNAaA0pYtpeJF/PbP+B7B3bieIT1bdHywNhiPpTxKXZJE//bDv3jJR8kksV0XjIod6DYI+mm+iP/HhMkllNLRAIYD0AHYRQj5mRCSca7nHThwAA8//DDy8vIQEhKCpUuXnvK40+nE5MmT8eOPPyIzMxPl5eUnHnv55ZcxdOhQ5OXlYeLEiSgpad4OoKCgAIsXL8aWLVuQlZUFnucxf/58AIDNZsPAgQORnZ2N4cOHY/bsM1f0P9d5T7ZgwQKMGzcOWVlZyM7ORlpaGgDg9ddfx+7du5GTk4MNGzYgJ+evpX2Cg4ORm5uLRx55BI8//vgZ57z//vvx0UcfITMzEzNnzsRDDz0EAJg6dSqmTJmC3NxcREdfeHoQSyiXIZNOePiGPh1cie3Vm+xIACyd1EcKrjlAat+8mWuc/TixLv8A5Kb3geTx/lVN0QcBtPVLxlwytx1C4a/klu43Uw13/i2cO5k74V9pjyOn6hD+vuIpXmzFPBZvIiCYNvBBm5YTngLQcMEnKIxSWkgpfQLNEya/QXPt5azi4+NPXIz79u2L4uLiUx7ft28f4uPj0b17dxBCcOedd554bOPGjSd+Hj9+PEJDm1eQXrNmDTIzM9G/f3+kpaVhzZo1OHSoeS6TVqs90QdztnjnO+/J+vfvjzlz5uCll15Cbm4uLJbm7/E333yD9PR09OnTB3l5ecjP/2sjt9tuu+3Ef7dt23bK+axWK7Zu3Yqbb775RK2qrKwMALBly5YTz73rrrvO9VKewOahXJ6qdQL3yGsTk/7z9892miQVJrcuui9VinIcI7WvXsfB0zwfxbrkTbiL9qDdE18BHXpJdM27PGQfXMhPp7cAso82F9y/CrrOg+SrO1+FHw7/eNZ2yfSIdHpj1xvI7Jyl+DLvO58Oob03+QYxxhJVyHP8HF+W43SUUgea1wo7J53ur+4WnudPNHldYlzcfffdmDFjxhmPaTQa/Dnimed5iOLFfdaHDx+OjRs34qeffsI999yDJ554AsOGDcPMmTOxa9cuhIaG4p577oHT+de8r5NHWp8+6lqWZYSEhCArK+us8VozSpvVUC5THCHzIsy63NsHxih+5fzqrt5SHKnlal+6hqOuU9fBcv/+Gyqn9idyp74gt38uw+SbAQOn0JkBSfRZrUnY9jk/ICqD72jqcMrvecLj5m43yTfEX4d/rX8HX+Z956MSNusd3h3/7P03h0ljvAEBuL1vQkICiouLUVRUBABYuHDhiceGDx+OBQsWAAB++eUX1NU1z7kcPXo0lixZgsrKSgBAbW0tjhw5gpY613lPduTIEURFRWHy5MmYNGkS9uzZg8bGRphMJgQHB6OiogK//PLLKc9ZvHjxif8OGnTqJqZBQUGIj4/Ht99+C6A5KWZnZwMAhgwZgkWLmteB+7Pp7nxYQrl8UbNeuOvB4fHu6GC9YkE+uSVZTjI6uNppYwm1n71FRK49jsopiby7spySu+cCXYcqVp4W0ZlVHOF1FtYK8CW76G09bj2xCGSEIQKPpz4mx5njcf0PU8n2smzflQ+AWWPEu1c8YzcIursBHPVpYRSi1+sxa9YsjB8/Hunp6YiMjDzx2PTp07Fx40YkJydj2bJliI1tXtElKSkJr732GsaOHYuUlBSMGTPmRPNRS5zrvCdbv349UlNT0adPHyxevBhTp0498XNCQgJuv/12DBky5JTn1NXVISUlBR988AHef//9M845f/58fPHFF0hNTUVycjKWL18OAPjggw/wySefoHfv3jh+/PgFy8/W8rrMuUT5hZxjDc8+8PXvXp8k9vaNifLIDoRUP3sFketa9qUyjPkngv/xKsXRPTJd9Q4Ph3q7C56QeBXI0EkyXf2qT2+4PNe8Ia8u2wwOHEbHjOLWluykz2/5kPhiWPDpZg5/yjEwOmWhUWP4p6/L0lJJSUkH2dIr3pWUlFSUn5/f7c+fWQ3lMqcTuLeSoi1H7h3S2audFy+O705Hxmi4mheubHEyAQDHqi9QPqUXEYkF5L6FQM/R6l89dSZA8vj8qq0pXMVd3fkqblD0IDyw+mU8t+UDv0gmN3QdRQdEp5QZNYZHfF0Wxr+whMJ4TDph3KShcU3Dunun/+KJK7vR63paSM20sZCqzhz2eEHWWlT/+wq+8X/Pg4x6EuTmj2SEq3hjqTUqvzDkeeObQNLvkkjSBIi1ZXR/XQmyq1Sb3nFe8UEd8XT/ex0mjeFaAKqvDcf4N5ZQGAA4ZtDy17wxMdkeH268pBPdPzwOt6eGkprpV0MqO3MCVWvYV89B+eRuxFlSRMht/wGZ8KqEYBW2ytAafVND4TVAlysoGfsyRJhQObU/aqb2Jb1C4smVsQNVL87ptJwG74/4P5uGE/4FIP+CT2AuO2zYMPOn7XoN//Cnd6R9csvnO42NF7F1750DY3B/RiRqX7kWYomX9uBwO1D/wb2EC22PkMfnQPuPr4GCXyW67UsethrvxDid1ghIKs771IeAdB0hIX4oLzdU0bq3byfu7NUnhgy75z5PXrj7dewoz0XTpe4WeQme7HePq50heJ3ACWfOyGsDrFZryenLrTOXxmq1ntIEwTrlmVPY3eJHheXWe++f+3ur5qdMTIvG82NiUTfjJrgLvL424Al8p0SETPlE1sT15nB4q0R3zpZtlQYAABrcSURBVONR6eXmoHHPgQRFiHT3V8recIV2BukxVkRUoiBVHRMbZv9LcOeuO+uhQe/tkNa4yvHStk99smbOlbED8dKghyuNGn1PAD4YKcG0BSyhMKcTrC5x7U855RlvrdzfooX2xiVH4vXxXVA/8064ss9cpVUJXLuOCJ70LtWljCBoqpRp5iIOhWub94a/VBNeAdFoJZq10PsXb8IBHVJBel4lwRjGuwt3So1znuHFo+dvQeLCOiHow0w8vPYNZFXt83qxzmdA+xS8N+IZq0HQXQFgj6rBmTaFJRTmbELsbjH3nV8PdFieVXbefrZh3cPw/g3d0fDxZDh3/qhW+f4iaGG+4UkYR98pccFRPI5nS7TgNx6HtgDOpos758S3QUS7TPd+550+RsIDYV1BOqTI6NSPo5IoOzYs4hrnTwfcLU+AljtfRdOo2+gNP0wlHpVWFEiN6IlPR79gNwj6cQA2qxKUabNYQmHOpafDLe1+aH6WOfvY2Sck9uscgv/8PQFNsx+njk2LfL6LHhceA/Pfnoah71UiMbcTUHVApvtWcyjNBaoOtng5FXLzB4C1HHTfzxdfGFM4EN4NJDpNREQPgbrtsrtwF7H9/ClxZ6+56NMGfV4ozSvZRD7P/VbxATU9Q+Pw37GvOEwaw0QAvyodj2n7WEJhzudqq1Nc+s//ZRoOVJ7aGZzcwYKv7kii1q+fh33Vlz5PJqfjgsJhvHoK9P2vloWIGAKtgaDmiISSTEJLcznUlgCNZYB4Zuc7ufUzSusOEuxfdeFAGgNgCG3+ZwoHiewpIawbD8JBqiuX3PlbeNuKj702SEHo1heWV1bitp+fwZHGUq+c82zigjrgf1e94TBpDHdxhFt64WcwDEsozAXIlP7d5hLnTJr7u/FAhRUA0CXCiEV396L2pW9R2w8ftImh53x0V+gHToQ2eSjVdOwuc6YQDlojgccJWCsl1JdSNJRycDVx6DWBorGU0Kp9py5jbwihxBQpwRQGGEIIdBYeIIDootRlk6X6KuLet51zbFwIz/6div0tQU/Oo8Vdk+hdK/+tyGsfbYrAvKvftAdpTQ/zHP+VEjGYwMQSCnNBMqU32V3S3En/3969h0VZp48ff39mBhgOgqZSup7IWBAUPEKImkia/bDctawsS7dvVp7W3K/llhqedq1d99uWh2+XHdQ8JJ6utFYrLamvhzTwlJG6sqJtmSXgYYbTDHP//hicBQFFBB6Qz+u65lLnmeczN48Xc8/zee7nc7+339de6GTDf0VJ4ZZFLtvaP9XPbotV5W3Fq300Xh2jsbQJx3xLa0z+TUUFNBNMClwiymQCkxmcRar4Uo7JlXsW57nvcf18Cse/9uM49S046/gmSJOFwKUnXfMPrlKbMnfU6NlhC9+mrLz31bymPk1e8jZ7vV6TY2s3P51QtCpxueSBvKLi97yK830dX6x0XVo2tWEnkwbOJ24YPhP+l6GbJpJTUDNtSIK8A3jv3nn2lr7N5lstPjNrZFCtUWkQ0xWa8UwmtSHAapnk7eMjhXs36WRisMI9G+HUt8UvxYypkWXjb7EG8fag2fYWvk3ftlp8ZtXEmFrjoxOKdj3eVhbve5q9tDHPp9s9RsfS6F2aN9wcd1sXU+9WXW9onLBmHVg75G95vwoIft3XYp0M9WAFSq1B0glFu17bTT5+A5r+YflF38TRxjQx1wCQS9k4NvxVzeo9XqzmKt2DWs7d7eLk3Xvm5DX1aTLaavGZhk4m2g3Q11C06vq1K9+2rWDX+uAL7/y3lWIDG1I1coELDxd/kP0tf0tfVuWpSIVibPTDjsc6JV3wtVgHAQdqMUStkdBnKFp1HTf5BnSx9h62s/ncbXZTUPC199BqhX3ecPOwOxLNYc06VOn1VrMPr/Wfmjci/P9l+FqsndHJRKshOqFoN+KiyS/wHq824a+3/NvefK+O3Y2Op1Eq/uEYxf+3Vv4cP8llUlf/lW7l34LVSX+x97g1YrO/l28scLZuotQaA51QtBvlUj5+00yBzR9tPnOL3bf/SD2HaoBLb05QwRZfRoTdW+nx79oynDVJ8/Nb+7ec5e/l+6hSapRSqnyDcU2rJn0NRatJEa5825aiI1+0vPDmBD/XpVrqV6JVyDtqAH5T1/DAh5P5Ke+c53mTMjEi7N7icV1H5PtafB4CtgIopQKBvcD/iEiD7HGi1S86oWg1zeoqsL9KsWPM+cVjfQu//ofR8TQqgTM2u75t2Zxnt882AbQPbM2f+0yytw247ViAt9/DQJk2mkqpMOD/gN+IyG4DQtZuIjqhaLWljyvfllJ44JNmF96a7Ct23ZOpTnj7EfTOCZm97y11m3+L4meiHiq0mMwveZksC4AKy7yVUknAEiBGRH6o03i1m4pOKFpt8nfl217DWTTy/MKnfQsPfGp0PI1CwIhk/H8zmTxHwdcB3n6PAP+61j5KqZeAocBdIlJQ60FqNyWdULS6MMBVYFvp+Gda4MV3p/g7f6jhlr0aAMoaQJNHXi70S3yiCIvPLGU2v0YlZyXl9lVKAWuBi8BToj8YtGrQCUWrKz7iKJqIyzkz78s1ZtuaOVZ90b6GmC34JYyUJiNm5mO2fGjyC5wI/HK9wyilAoDdwBIRWVjjcWo3PZ1QtLrWwpVv+xPwuG3Dq972Lf9rrvPl328WyoQ1/kECR86xKx+/b0z+Qc/hrtqq/pBK3Q7sAR4WkdSaCFNrPHRC0YwS7rJfWCSOgljb2j/55X3xvsJRvnuiVjGfXkMIfOLPNlOTZidNfkGTgB01NbZSaiDwHhArIqdralzt5qcTima0fi77+TlAL/uHC7ztn7xtFnuu0THVT8qET4/BNHnkZZu5RZufTH6BzwFbqIUFHZVS/w08BvQRkbyaHl+7OemEotUXka68izOU2TI0b8cKk33zG97F5743OqZ6wdSkOb6Jo4r9k8YXKIv3KZN/0ExgA1W84F4dJRfpVwAKGKkv0mtVoROKVt/8ylVgn6JMpqcLD6dK3mdL/QsPbodip9Fx1Tmvjj3wHzI+z9orySROxwcmv8D5QHpdvb9SyhfYCawWkb/V1ftqDZdOKFq9o5RqJyIXRFyPiP3CeEyW0Pyda1X+jpU+jsw6+zw1hLlFW6yx94tv4iibuXmbfOXl8z/K4vU2YEhJnFKqHe4L/aNERN9IpF2VTihavaKUuhU4CrQUkcunJbeLo/AJcRY9LXkXm+R9tsy34KtNZue/jxoYac0x39YR651DXb79Rtgswe3M4nRuMvk1WQFsA2qkxe+NUEr1A9YBvUUk0+h4tPpLJxStXlFKTQM6iMiYijYDMa58238Bv6HY4V94cDsF6R/7FX2zA9fFcxXsUg95++J9Rw+8O/cr9u3zUJ652W0iruL1Jt8mq4EvgHo3v6eUGg88C8SJiM3oeLT6SScUrd5QSlmAk8B9InLwWi8HOgKDXLacYcrHv3fxuX87CtK2+BYd3ePlPHWE4p+zajvkKlEBt+AdfifeEX0cPlEJeZbWoX5SYD+hvK2fKm/fDbhvJjT8TORqSi7Svw0EAcP1RXqtIjqhaPWGUuoB4DkR6VuN3S1AjDgd90j+xf54+XRWJnOA88cT+Y4TadaizAM+zlNHcJ45QW0tVGlq0hxz61Asre/A8qswp1f7LnmWNuHKFNjcWwrsB5Vf4FZltnwJ7FVKOYFwETlcK8HUAqWUD5AKfCQifzI4HK0e0glFqzeUUjuAN0UkpYaGbA5EAdEu+/neCD2Uj9+vQEyuC78UFGf/4CrOOWN25fzoU3z+Jy/Ju4g4HeAqRoqd7soyl9P9d6Uw+QVhCmiG8m8qpsAWhaaglg5Tk+YuU2ALzC3aWJXZ7JKCvFMoU4byD9yvlOkYcAz4jiumsUpd7G7dkL7tK6VaA18Dz4jIR0bHo9UvOqFo9YJSKhL3RegOIlLba7EEAW1LHsFAS3EUtBZHYUtcLi9EvAAvEAuCF4gXKDCZcrB4n1U+fj8ppXKBXCCn5JGJe/2sKv9CKaX+BdwvIkdq+gesTUqpOGAT0E9EGkxlRLt27T4PCAhoZ3QcDZnNZjt9+vTpAZVtt9RlMJp2FeNxL0pYFwt7XSh5eD7IlZcV5WWtg7cuYxswsHQcDYGI7ClZ7v4DpVSsiFwwOqaqCAgIaJeRkdHR6DgasoiIiKtu1z3lNcMppYKAEbibPDUmlxNKgyMibwOfASuVUvpzRAN0QtHqh1HAJyLyo9GB1LHPgT4lF7sbosm4pw9nGR1IXZg5cybz588v93xWVhadO3cGIC0tjd///vfVGj81NZUhQ4YAsHnzZl555ZUbGuNKHTp04Ny5qpfWL1u2jAkTJlzX++spL81QJd9uxwNPGR1LXRORHKXUUSAOd/VUgyIiRUqpB4GvlVIHRWSD0TEZrWfPnvTs2fOGx7n//vu5//77ayCiuqXPUDSjJQIFuNeMaowa7LQXgIj8DAwD3lRKdTY6nqrKysoiPDycxx57jE6dOvHggw+Sl+deVLn0N/m0tDT69+/v2e/QoUPExcURGhrKW2+9VW7c0mcINpuN3/3ud3Tp0oWoqCg2bCifbz/++GPCw8Pp3r07Gzdu9Dxf+uxg3bp1dO7cmejoaPr16+eJv2/fvnTv3p3u3buze/duz74XL14kKSmJsLAwnn32WVyu8muIrly5kpiYGLp27cozzzxDcbH7NqilS5fy61//mpiYGHbt2nVdxxR0QtGMNwFY2JBKZ2tYg04oACKSjnv66wOl1C1Gx1NVx44dY9y4cXz33XcEBgayePHia+5z+PBhPv/8c/bs2cPs2bP58cfKZ2nnzJlDUFAQ33zzDYcPH2bAgLLFUQUFBYwZM4YPP/yQ9PR0fvrppwrHmT17Np988gmHDh1i8+bNAAQHB7Nt2zb2799PSkpKmWm2ffv2sWDBAjIyMsjMzCyTqAC+++47UlJS2LVrFwcPHsRsNrNq1SrOnDlDcnIyu3btYufOnWRkZFzzeFxJT3lphlFKdQDigUeNjcRQe4BwpdQtIpJjdDDVJSIrlVLdcCeVD42Op5SdIrKnog1t27YlPj4egJEjR/LGG28wZcqUqw42dOhQfH198fX1JSEhgX379tG1a9cKX7t9+3bWrFnj+XezZs3KbD969CghISGEhoZ6YliypHxdSnx8PKNHj+ahhx5i2LBhADgcDiZMmOBJCMePH/e8PiYmhttvvx2AESNGsHPnTh588EHP9s8++4z09HR69eoFQH5+PsHBwezdu5f+/fvTsmVLAB5++OEy41aFTiiakcYCy0XEbnQgRhGRQqXUTmAAsN7oeG7QVGAK7nt76ovAyja4V5Mp/2+LxeKZJiooKKjSPrXpzTffZO/evfzjH/+gR48epKens2DBAm699VYOHTqEy+XCav1Pyfu1YhQRRo0axbx588o8/8EHH9xwrDqhaIYo6bXxJO4L0o3d5WmvBp1QSlaHvv7SJIOcPn2aPXv2EBcXx+rVq+nTpw/gvoaSnp7OvffeW+66x6ZNm3jxxRex2+2kpqbyyiuvUFRU8a1TAwcOZNGiRfz9738HIDc3t8xZSnh4OFlZWWRmZtKxY0fef//9CsfJzMwkNjaW2NhYtm7dyvfff8+FCxdo06YNJpOJ5cuXe66BgHvK6+TJk7Rv356UlBSefvrpMuMlJiYydOhQJk+eTHBwMDk5OVy6dInY2FgmTZpEdnY2gYGBrFu3jujo6Os6pvoaimaUh4GvReSE0YHUAw3+OkpDFBYWxqJFi+jUqRO5ubmMHTsWgOTkZCZNmkTPnj0xm81l9omKiiIhIYE777yTGTNm0Lp160rHnz59Orm5uZ4L6jt27Ciz3Wq1smTJEpKSkujevTvBwRWf2D3//PN06dKFzp0707t3b6Kjoxk3bhzLly8nOjqao0eP4u/v73l9r169mDBhAp06dSIkJITf/va3ZcaLiIhg7ty5DBo0iKioKAYOHMiZM2do1aoVM2fOJC4ujvj4eDp16nRdxxP00iuaAUpWrk0DZojIFqPjMVrJ8fgB6Kv7jdSeiIiIE5fvlM/KymLIkCEcOdKgFikwXERERGZGRsYdlW3XZyiaEWKBpsDHRgdSH5RUuG0DBhkdi6bdCJ1QNCOMBxaLSPkC+cZLT3vVoQ4dOuizk1qgE4pWp0pa/A4BlhodSz2zHUgoaTKmaQ2STihaXXsKWN+Q77moDSLyE/A9cOPrdmiaQfS3Ia3OlHz7fha4z+hY6qnL015fGR3Izchms52+1vLr2tXZbLbTV9teaZWXbkZzY67ViKYxKmnxO1lE+hgdS32klBoMvCQi/YyORdOqo9IzFN2M5sbob0IVGg8sNDqIeuxLYJ1SqomIXDI6GE27XvoailYnSlr8hgMbr/XaxkpE8nD3a7/L6Fg0rTp0QtHqSl22+G3IdPmw1mDVWkKp7e5mN8LormaNTSNu8VsdOqFoDZahVV411d1Mq/eeAD5thC1+q+MAEKyUaiMi/zY6GE27HlVKKFlZWQwePJgePXqwf/9+IiMjee+99/Dz86NDhw6kpaXRokUL0tLSmDJlCqmpqcB/upudO3eOF154gTFjxpQZNzU1lfnz5/PRRx9hs9mYOHEiaWlpKKVITk7mgQceKPP69PR0/vCHP2Cz2WjRogXLli2jVatW9O/fn9jYWHbs2MH58+d555136Nu3L1lZWTz++OPY7e7V0RcuXEjv3r2B/3Q1O3HiBAkJCSxevBiTqewJ28qVK3njjTcoKioiNjaWxYsXYzabWbp0KfPmzaNp06ZER0fj49NQW4LXvpIWvxNohC1+q0NEipVSnwN3A8sMDuemoitXa05lVaxVPkM5duwY77zzDvHx8Tz55JMsXrz4ms1oDh8+zFdffYXdbqdbt24kJSVV+trS3c3AvdRzaQ6Hg4kTJ7Jp0yZatmxJSkoK06ZN49133wXA6XSyb98+tmzZwqxZs9i+fbunq5nVauWf//wnI0aMIC0tDXAv8ZyRkUH79u0ZPHgwGzduLNOEpnRXMy8vL8aNG8eqVasYOHAgycnJpKenExQUREJCAt26davwZypZ9M/3mgf35tafxt3itzouT3stMziOm4quXK05lVWxVjmhGN3d7NixYxw5coSBA93Ty8XFxbRq1cqz/XInsx49epCVlQXUi65micDmqx6km58ATzXiFr/VsQ2Yq5Qy6fXOtIakygnF6O5mIkJkZCR79lTYzdMz7WQ2m3E6nQC89tprhnY1E5HtgF+Vd9A0QESylFIXgS7AIaPjuRldbfn6l19+mX79+nH33XeXeb70FP2VSk/91xeVxTRz5kwCAgKueUJQWkBAADab7Zqvq3KV1+XuZkCF3c2ACrubFRQUkJ2dTWpqqufbfkUudze77Mopr7CwMH755RdPDA6Hg2+//faqMV+4cIFWrVphMplYsWJFhV3NXC4XKSkpnp/nssTERNavX8/PP/8MQE5ODqdOnSI2NpYvvviC7OxsHA4H69atu2oMmlZNejl7g8yePbtcMtGqpsoJxejuZt7e3qxfv56pU6cSHR1N165d2b1791Vjrq9dzTStCnT5cC0rLi5mzJgxREZGMmjQIPLz8wEYPXo069e7uzF//PHHhIeH0717dzZu/M89udnZ2QwaNIjIyEieeuopSs/orly5kpiYGLp27cozzzzj+SIbEBDAtGnTiI6O5s477+Ts2bPlYrLb7Tz55JPExMTQrVs3Nm3aBLhvTxg2bBiDBw8mNDSUF154wbPP2LFj6dmzJ5GRkSQnJ5cZ7y9/+QtdunQhJiaGEyfKN0fNzMz0FFz17duXo0ePAnDy5Eni4uLo0qUL06dPr/pBFZEKH506dTohJU6ePCmRkZGiVV3J8av0+OqHflztgbsB2SXAanQsN8vjys80s9ksBw4cEBGR4cOHy4oVK0REZNSoUbJu3TrJz8+XNm3ayPHjx8Xlcsnw4cMlKSlJREQmTpwos2bNEhGRjz76SAD55ZdfJCMjQ4YMGSJFRUUiIjJ27FhZvny5iPs/VTZv3iwiIs8//7zMmTNHrvTiiy964sjNzZXQ0FCx2WyydOlSCQkJkfPnz0t+fr60a9dOTp8+LSIi2dnZIiLidDrlrrvukkOHDomISPv27WXu3LkiIrJ8+XJP7MnJyfLXv/5VREQGDBggx48fFxGRr776ShISEkRE5L777vPEvXDhQvH39y8TZ2Wfb3q1YU2rh0TkvFLqG+CTkuspWvVsEJFlFW0ICQnxFAmVLua57OjRo4SEhBAaGgq4i5GWLHHfm/vll196zliSkpI8RUSVFfOAe5bl8g3VPXr0YNu2beVi+vTTT9m8ebPnpvCCggJOn3Yv8JuYmEhQUBDgnkE5deoUbdu2Ze3atSxZsgSn08mZM2fIyMggKioKcBccXf5z8uTJZd7LZrOxe/duhg8f7nmusLAQgF27dnkuYTz++ONMnTq1woN7pSolFN3dTNMM8QSg51RvTKUXWkvfP2Y2mz1TXjdCpOJiHgAvLy9P8U/p4qEr99+wYQNhYWFlnt+7d2+5eJ1OJydPnmT+/Pl8/fXXNGvWjNGjR5cpjipdbHRl4ZHL5aJp06YcPHiwwp+lOkVU+gxF0+opETkBlJ/41upEeHg4WVlZZGZm0rFjR95//33Ptn79+rF69WqmT5/O1q1bPUVEiYmJDB06lMmTJxMcHExOTg6XLl2iffv2VXrPe+65hwULFrBgwQKUUhw4cKDS+9zAfYO2v78/QUFBnD17lq1bt9K/f3/P9pSUFP74xz+SkpJCXFxcmX0DAwMJCQlh3bp1DB8+HBHh8OHDREdHEx8fz5o1axg5ciSrVq2q8jGrNKHoZjQ35lqNaDRNq9+sVitLliwhKSkJPz8/+vbty6VL7q4CycnJjBgxgsjISHr37k27du4b8EsX87hcLry8vFi0aFGVE8qMGTN47rnniIqKwuVyERISUmGZ8mXR0dF069aN8PDwMvcKXpabm0tUVBQ+Pj5lEuJlq1atYuzYscydOxeHw8EjjzxCdHQ0r7/+Oo8++iivvvoqQ4cOreohq7zBlqZp2s0kIiLihL5TvmZERERkZmRk3HHl83r5ek3TNK1G6ISiaZqm1QidUDRN07QaoROKpmmaViN02bCmaY2CrlytOZVVseoqL03TNK1G6CkvTdM0rUbohKJpmqbViP8P3aaGCsa+FNYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot general overview 2nd attempt: group deleted, disabled, enabled\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "labels = ['public deleted', 'hidden deleted', 'public enabled', 'hidden enabled', 'public disabled', 'hidden disabled']\n",
+    "vals = np.array([[num_deleted_public, num_deleted_hidden], [num_enabled_public, num_enabled_hidden], [num_disabled_public, num_disabled_hidden]])\n",
+    "wedge = 0.3\n",
+    "\n",
+    "cmap = plt.get_cmap(\"tab20c\")\n",
+    "outer_colors = cmap(np.arange(3)*4)\n",
+    "inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))\n",
+    "\n",
+    "ax.pie(vals.sum(axis=1), radius=1, colors=outer_colors, \n",
+    "       wedgeprops=dict(width=wedge, edgecolor='w'))\n",
+    "\n",
+    "wedges, texts = ax.pie(vals.flatten(), radius=1-wedge, colors=inner_colors,\n",
+    "       wedgeprops=dict(width=wedge, edgecolor='w'))\n",
+    "\n",
+    "ax.set(aspect=\"equal\", zorder=0)\n",
+    "\n",
+    "bbox_props = dict(boxstyle=\"square,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.72)\n",
+    "kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n",
+    "          bbox=bbox_props, zorder=1, va=\"center\")\n",
+    "\n",
+    "for i, p in enumerate(wedges):\n",
+    "    ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
+    "    y = np.sin(np.deg2rad(ang))\n",
+    "    x = np.cos(np.deg2rad(ang))\n",
+    "    horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
+    "    connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n",
+    "    kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
+    "    ax.annotate(labels[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),\n",
+    "                horizontalalignment=horizontalalignment, **kw)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "#fig1, ax1 = plt.subplots()\n",
+    "#ax1.set_prop_cycle(color=['lightskyblue', 'steelblue', 'yellowgreen', 'olivedrab', 'lightgrey', 'grey'])\n",
+    "#ax1.pie(sizes, labels=labels, autopct='%1.1f%%', explode=(0, 0, 0.1, 0.1, 0, 0), startangle=90)\n",
+    "#ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 105,
@@ -257,7 +316,7 @@
     }
    ],
    "source": [
-    "# global filters\n",
+    "# not global filters\n",
     "print (len(df.query('af_global==0')))"
    ]
   },
@@ -542,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,9 +665,49 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This diagram depicts all filters. 2/3 of them are deleted/disabled. Often, before a filter is disabled all its actions but \"log\" are deactivated. So it makes sense the diagram shows a lot of \"log only\" actions."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# What are the actions of all active filters\n",
+    "active = df.query('af_enabled==1')\n",
+    "active_actions = collections.Counter(list(active['af_actions'].fillna('log only'))).most_common()\n",
+    "\n",
+    "active_labels = [x[0] for x in active_actions]\n",
+    "active_values = [x[1] for x in active_actions]\n",
+    "\n",
+    "plt.xlabel('actions')\n",
+    "plt.xticks(rotation='60')\n",
+    "plt.ylabel('Num filters')\n",
+    "plt.bar(active_labels, active_values)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -628,12 +727,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -649,7 +748,7 @@
     "ah_actions_values = [x[1] for x in active_hidden_actions]\n",
     "\n",
     "plt.xlabel('actions')\n",
-    "plt.xticks(rotation='20')\n",
+    "plt.xticks(rotation='60')\n",
     "plt.ylabel('Num filters')\n",
     "plt.bar(ah_actions_labels, ah_actions_values)\n",
     "plt.show()"
@@ -1602,67 +1701,67 @@
     {
      "data": {
       "text/plain": [
-       "10.68.16.39                               1689\n",
-       "37.113.52.15                              1249\n",
-       "95.152.44.52                              1133\n",
-       "5.165.178.194                              715\n",
-       "94.181.143.10                              697\n",
-       "95.152.42.158                              674\n",
-       "5.166.250.109                              559\n",
-       "5.166.224.152                              556\n",
-       "Acheter cialis                             533\n",
-       "37.113.51.96                               473\n",
-       "93.124.7.25                                430\n",
-       "5.167.114.39                               317\n",
-       "93.124.34.23                               307\n",
-       "36.250.176.0                               291\n",
-       "Wwekik2222kdjdj                            279\n",
-       "37.113.37.111                              274\n",
-       "94.181.170.143                             247\n",
-       "Theadityapratap                            239\n",
-       "94.181.156.128                             229\n",
-       "Achat cialis                               222\n",
-       "93.124.46.78                               222\n",
-       "93.124.28.116                              213\n",
-       "176.97.116.140                             202\n",
-       "5.165.186.39                               192\n",
-       "Acquistare cialis                          190\n",
-       "93.124.74.221                              187\n",
-       "AbhiJahazi                                 186\n",
-       "37.113.34.32                               186\n",
-       "37.113.28.187                              185\n",
-       "64.62.219.98                               180\n",
-       "                                          ... \n",
-       "2602:306:3449:D8B0:1D59:35A1:E776:9D24       1\n",
-       "65.30.148.79                                 1\n",
-       "100.11.133.143                               1\n",
-       "178.20.148.10                                1\n",
-       "CanalesHakansonKLaM                          1\n",
-       "BreslinMuckleoAhV                            1\n",
-       "CalisePopielsxeFt                            1\n",
-       "2605:E000:5B04:C000:F8CB:904:9135:B38C       1\n",
-       "2601:981:C002:EE25:B01D:3AD9:A364:7018       1\n",
-       "Flyingfalconer                               1\n",
-       "CharliBaughman0                              1\n",
-       "Nicholaslimmm                                1\n",
-       "Elvis783929030                               1\n",
-       "207.170.199.15                               1\n",
-       "166.170.57.137                               1\n",
-       "Cagonhenrio                                  1\n",
-       "FawnHib4113                                  1\n",
-       "110.138.157.45                               1\n",
-       "66.177.171.107                               1\n",
-       "Deepanshubhati                               1\n",
-       "BernardiAllgeyergBLB                         1\n",
-       "197.156.81.2                                 1\n",
-       "2605:6001:E605:5700:7527:2E8C:D4A:EB12       1\n",
-       "81.148.221.230                               1\n",
-       "Marems79                                     1\n",
-       "14.98.111.171                                1\n",
-       "2601:1C0:4000:F7:411F:C606:F304:B28C         1\n",
-       "VincentFrisina                               1\n",
-       "FranceBramwellMyFNS                          1\n",
-       "Abhishekhegde11502                           1\n",
+       "10.68.16.39                                1689\n",
+       "37.113.52.15                               1249\n",
+       "95.152.44.52                               1133\n",
+       "5.165.178.194                               715\n",
+       "94.181.143.10                               697\n",
+       "95.152.42.158                               674\n",
+       "5.166.250.109                               559\n",
+       "5.166.224.152                               556\n",
+       "Acheter cialis                              533\n",
+       "37.113.51.96                                473\n",
+       "93.124.7.25                                 430\n",
+       "5.167.114.39                                317\n",
+       "93.124.34.23                                307\n",
+       "36.250.176.0                                291\n",
+       "Wwekik2222kdjdj                             279\n",
+       "37.113.37.111                               274\n",
+       "94.181.170.143                              247\n",
+       "Theadityapratap                             239\n",
+       "94.181.156.128                              229\n",
+       "Achat cialis                                222\n",
+       "93.124.46.78                                222\n",
+       "93.124.28.116                               213\n",
+       "176.97.116.140                              202\n",
+       "5.165.186.39                                192\n",
+       "Acquistare cialis                           190\n",
+       "93.124.74.221                               187\n",
+       "37.113.34.32                                186\n",
+       "AbhiJahazi                                  186\n",
+       "37.113.28.187                               185\n",
+       "64.62.219.98                                180\n",
+       "                                           ... \n",
+       "39.54.130.54                                  1\n",
+       "VerrillSabinenEeci                            1\n",
+       "94.72.204.28                                  1\n",
+       "59.101.86.233                                 1\n",
+       "Valde21                                       1\n",
+       "Frank L Mason                                 1\n",
+       "71.107.27.56                                  1\n",
+       "76.69.22.109                                  1\n",
+       "Fbdave                                        1\n",
+       "Lvtd                                          1\n",
+       "95.105.29.221                                 1\n",
+       "24.180.180.56                                 1\n",
+       "2001:558:6017:144:7422:40DB:D32E:FDB1         1\n",
+       "Ahmadvc                                       1\n",
+       "Josephine18                                   1\n",
+       "NZ Raven                                      1\n",
+       "McaleerKoganGhOOO                             1\n",
+       "2600:1006:B123:3E61:79FA:2751:C2D2:69BF       1\n",
+       "Laurenbrown44                                 1\n",
+       "MarkeeStillionsDjntn                          1\n",
+       "31.54.171.137                                 1\n",
+       "Romp48                                        1\n",
+       "Moverhleo                                     1\n",
+       "188.39.41.194                                 1\n",
+       "Rainbow Joint                                 1\n",
+       "Fateh Ali Samejo                              1\n",
+       "135.23.188.13                                 1\n",
+       "2600:1012:B019:2196:2C2A:154A:5E7D:4489       1\n",
+       "UmfleetVanhoffCbdjL                           1\n",
+       "124.189.132.154                               1\n",
        "Name: afl_user_text, Length: 139586, dtype: int64"
       ]
      },
@@ -6660,84 +6759,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "527    71853\n",
-       "61     27072\n",
-       "650    24264\n",
-       "633    21099\n",
-       "279    18460\n",
-       "636    17279\n",
-       "384    15080\n",
-       "135    10028\n",
-       "30      7829\n",
-       "172     7471\n",
-       "271     7192\n",
-       "380     6554\n",
-       "80      6530\n",
-       "364     6238\n",
-       "686     6089\n",
-       "712     5597\n",
-       "466     5555\n",
-       "432     5398\n",
-       "220     5385\n",
-       "550     5215\n",
-       "189     4730\n",
-       "3       4656\n",
-       "148     4470\n",
-       "260     4148\n",
-       "614     4120\n",
-       "231     3398\n",
-       "631     3349\n",
-       "225     3245\n",
-       "46      3174\n",
-       "680     3134\n",
-       "       ...  \n",
-       "242       19\n",
-       "706       19\n",
-       "16        19\n",
-       "734       18\n",
-       "264       18\n",
-       "710       17\n",
-       "666       15\n",
-       "722       14\n",
-       "167       13\n",
-       "294       10\n",
-       "624        9\n",
-       "727        8\n",
-       "651        8\n",
-       "637        8\n",
-       "745        6\n",
-       "52         6\n",
-       "674        6\n",
-       "748        5\n",
-       "690        5\n",
-       "2          5\n",
-       "597        4\n",
-       "579        4\n",
-       "709        4\n",
-       "68         4\n",
-       "554        3\n",
-       "749        3\n",
-       "596        1\n",
-       "718        1\n",
-       "459        1\n",
-       "694        1\n",
-       "Name: afl_filter, Length: 138, dtype: int64"
+       "count      138.000000\n",
+       "mean      2702.224638\n",
+       "std       7454.825274\n",
+       "min          1.000000\n",
+       "25%         25.250000\n",
+       "50%        250.500000\n",
+       "75%       2799.000000\n",
+       "max      71853.000000\n",
+       "Name: afl_filter, dtype: float64"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Number of hits per filter\n",
-    "df_jan2016['afl_filter'].value_counts()"
+    "df_jan2016['afl_filter'].value_counts().describe()"
    ]
   },
   {
@@ -6842,7 +6888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -6856,7 +6902,7 @@
        "Name: afl_action, dtype: int64"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6868,6 +6914,116 @@
     "df_sep2016['afl_action'].value_counts()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "527    24743\n",
+       "61     21609\n",
+       "384    18895\n",
+       "633    16897\n",
+       "650    16766\n",
+       "636    15372\n",
+       "135     8195\n",
+       "172     6582\n",
+       "3       5206\n",
+       "432     5005\n",
+       "380     4814\n",
+       "30      4801\n",
+       "712     4520\n",
+       "364     4519\n",
+       "686     4370\n",
+       "260     3342\n",
+       "279     3324\n",
+       "189     3103\n",
+       "231     3059\n",
+       "784     2893\n",
+       "98      2794\n",
+       "50      2745\n",
+       "550     2696\n",
+       "225     2667\n",
+       "631     2602\n",
+       "491     2317\n",
+       "752     2291\n",
+       "46      2223\n",
+       "132     2215\n",
+       "680     2053\n",
+       "       ...  \n",
+       "68        17\n",
+       "770       17\n",
+       "345       17\n",
+       "751       16\n",
+       "224       15\n",
+       "744       14\n",
+       "167       13\n",
+       "781       11\n",
+       "723       11\n",
+       "745       10\n",
+       "2          9\n",
+       "755        9\n",
+       "624        7\n",
+       "242        6\n",
+       "762        6\n",
+       "722        6\n",
+       "667        5\n",
+       "651        5\n",
+       "642        5\n",
+       "694        4\n",
+       "792        4\n",
+       "734        3\n",
+       "757        3\n",
+       "710        2\n",
+       "795        1\n",
+       "294        1\n",
+       "690        1\n",
+       "743        1\n",
+       "1          1\n",
+       "639        1\n",
+       "Name: afl_filter, Length: 139, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sep2016['afl_filter'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      139.000000\n",
+       "mean      1715.151079\n",
+       "std       4035.023411\n",
+       "min          1.000000\n",
+       "25%         25.500000\n",
+       "50%        231.000000\n",
+       "75%       1714.500000\n",
+       "max      24743.000000\n",
+       "Name: afl_filter, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Number of hits per filter\n",
+    "df_sep2016['afl_filter'].value_counts().describe()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -8096,6 +8252,15 @@
     "    print(df_actions_year.fillna('log only'))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO Multi line chart: a line per action"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -9318,6 +9483,15 @@
     "#TODO what's the difference between \"createaccount\" and \"autocreateaccount\"?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO multi line chart"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -10736,7 +10910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -10918,50 +11092,6 @@
     "    print(active_public_2nd[['af_id', 'af_actions', 'manual_tags']].fillna('log only'))\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'bot_vandalism': 'vandalism',\n",
-       " 'page_move_vandalism': 'vandalism',\n",
-       " 'image_vandalism': 'vandalism',\n",
-       " 'talk_page_vandalism': 'vandalism',\n",
-       " 'template_vandalism': 'vandalism',\n",
-       " 'link_vandalism': 'vandalism',\n",
-       " 'avoidant_vandalism': 'vandalism',\n",
-       " 'username_vandalism': 'vandalism',\n",
-       " 'silly_vandalism': 'vandalism',\n",
-       " 'trolling': 'vandalism',\n",
-       " 'hoaxing': 'vandalism',\n",
-       " 'prank': 'vandalism',\n",
-       " 'profanity_vandalism': 'vandalism',\n",
-       " 'religious_vandalism': 'vandalism',\n",
-       " 'politically_motivated': 'vandalism',\n",
-       " 'general_vandalism': 'vandalism'}"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clusters = {}\n",
-    "\n",
-    "vandalism = ['bot_vandalism', 'page_move_vandalism', 'image_vandalism', 'talk_page_vandalism', 'template_vandalism', \\\n",
-    "'link_vandalism', 'avoidant_vandalism', 'username_vandalism', 'silly_vandalism', 'trolling', 'hoaxing', 'prank', \\\n",
-    "'profanity_vandalism', 'religious_vandalism', 'politically_motivated', 'general_vandalism']\n",
-    "\n",
-    "for i in vandalism:\n",
-    "    clusters[i] = 'vandalism'\n",
-    "    \n",
-    "clusters"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 54,
@@ -11069,7 +11199,7 @@
     "for i in disruptive:\n",
     "    clusters[i] = 'disruptive'\n",
     "\n",
-    "pov = ['biased_pov', 'conflict_of_interest', 'stockbrocker_vandalism', 'self_promotion', 'seo']\n",
+    "pov = ['biased_pov', 'conflict_of_interest', 'self_promotion', 'seo']\n",
     "\n",
     "for i in pov:\n",
     "    clusters[i] = 'pov'\n",
@@ -11099,7 +11229,9 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -11191,6 +11323,24 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO Plot of parent categories!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO Plot (manual tags)*(hit count) for all filters"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
diff --git a/thesis/5-Overview-EN-Wiki.tex b/thesis/5-Overview-EN-Wiki.tex
index 31067fae581a1755316d7cede371e495c2750ed2..eb406814c9257ca0f198a202ac1a34d52cd0be2f 100644
--- a/thesis/5-Overview-EN-Wiki.tex
+++ b/thesis/5-Overview-EN-Wiki.tex
@@ -11,6 +11,8 @@ and the methods we use–in chapter 3.
 Section~\ref{sec:patterns} explores (syn) some patterns in the edit filters' usage and..
 And we look into the manual classification of EN Wikipedia's edit filters I've undertaken in an attempt to understand what is it that they actually filter in section~\ref{sec:manual-classification}.
 
+%TODO tell a story with the chapter: what do filters do? How have their tasks evolved over time (if feasible) --> maybe tell it along the peak: it is an extraordinary situation, where we can see it is exactly X and Y and Z what filters do
+
 %TODO check whether to discuss this bunch of random questions and where
 \begin{comment}
     \item how often were filters with different actions triggered? (afl\_actions) (over time) --> abuse\_filter\_log
@@ -91,7 +93,7 @@ The relative proportion of these groups to each other can be viewed on figure~\r
 \begin{figure}
 \centering
   \includegraphics[width=0.9\columnwidth]{pics/general_stats.png}
-  \caption{EN Wikipedia edit filters: hidden, disabled and deleted filters}~\label{fig:general-stats}
+  \caption{There are 954 edit filters on EN Wikipedia}~\label{fig:general-stats}
 \end{figure}
 
 \subsection{Filter actions}
@@ -166,6 +168,13 @@ It is signaled, that the mailing list is meant for sensitive cases only and all
 
 Oftentimes, when a hidden filter is marked as ``deleted'', it is made public. (examples!)
 
+$2/3$ of all filters are hidden.
+However, if we look at the enabled filters only, there are actually more public enabled than hidden enabled filters (or more or less the same amount)! (110 vs 91).
+This leads us to the hypothesis that it is rather that hidden filters have higher fluctuation rates, i.e. that they target specific phenomena that are over after a particular period of time after which the filters get disabled and eventually–deleted.
+This makes sense when we compare it to the hidden vs public filter policy: hidden filters for particular cases and very determined filters, public filters for general patterns.
+
+%TODO check hits: public vs hidden
+
 
 
 \section{Filter activity}
@@ -553,14 +562,21 @@ The maintenance cluster differs conceptually from the ``vandalism'' and ``good f
 
 \section{Manual tags discussion/manual tags + activity}
 
+
 \subsection{Manual tags distribution}
-%TODO discuss figure
 \begin{figure}
 \centering
   \includegraphics[width=0.9\columnwidth]{pics/manual-tags-distribution.png}
   \caption{Edit filters manual tag distribution}~\label{fig:manual-tags}
 \end{figure}
 
+%TODO discuss figure
+\begin{comment}
+* maybe just plot the parent categories and have a closer look at one of them exemplarily
+* maybe merge parent categories and only work with ``vandalism'', ``good faith'' and ``maintenance'' (and ``unknown'')
+\end{comment}
+
+
 \subsection{What filters were implemented immediately after the launch + manual tags}
 %TODO What were the first filters to be implemented immediately after the launch of the extension?
 The extension was launched on March 17th, 2009.
diff --git a/thesis/6-Discussion.tex b/thesis/6-Discussion.tex
index dd8783f54f5b7dd3efb689cfcc29bf46a725e844..9cba72813eed3d04ad080cbb62119a1c1ad77d37 100644
--- a/thesis/6-Discussion.tex
+++ b/thesis/6-Discussion.tex
@@ -22,6 +22,9 @@ Bot development on the other hand (syn!) is a little more challenging:
 A developer needs resonable knowledge of at least one programming language and on top of that has to make themself familiar with stuff like the Wikimedia API, ....
 Moreover, since regular expressions are still somewhat human readable and understandable (syn!) in contrast to a lot of popular machine learning algorithms, it is easier to hold rule based systems and their developers accountable.
 
+Filters are a simple mechanism (simple to implement) that swiftly takes care of cases that are simple to recognise as undesirable.
+ML needs training data (expensive), it's not simple to implement.
+
 \begin{comment}
 maybe it's a historical phenomenon (in many regards):
 * perhaps there were differences that are not essential anymore, such as:
@@ -87,6 +90,8 @@ propriate moderator tools."
 
 % TODO also comment on negative results! (what negative results do I have?)
 
+% TODO comment on: so what's the role of the filters, why were they introduced (to get over with obvious persistent vandalism which was difficult to clean up, most probably automated) -- are they fulfilling this purpose?
+
 %***************************************
 \cite{GeiRib2010}
 
@@ -200,3 +205,5 @@ also
 %TODO Do edit filter managers specialize on particular types of filters (e.g. vandalism vs good faith?) -- abuse\_filter\_history table is needed for this
 
 * talk to edit filter managers (especially such who are simultaneously also bot operators)
+
+* What proportion of work do filters take over: compare filter hits with number of all edits and reverts via other quality control mechanisms
diff --git a/thesis/appendix.tex b/thesis/appendix.tex
index a7293448f30130739db1c7270f53add7ac39420f..d33f67ce0ce64a0e519c20bef58b40570415c1de 100644
--- a/thesis/appendix.tex
+++ b/thesis/appendix.tex
@@ -232,10 +232,7 @@ Note: according to Wikipedia this behaviour constitutes harassment: "Posting ano
   Def: Hm.. I have the feeling all the filters here should be relabeled..
   Examples: 148 "Users creating autobiographies" (which should be rather "self\_promotion", I think); 894 "Possible Self-Published Sources" (maybe 'self\_promotion' is also more suitable here?); 878 "New user removing COI template"
 'conflict\_of\_interest'
-  Def: Filter targets people editing articles about themselves or organisations they are affilitated to or receive money from. (Compare 'stockbrocker\_vandalism', not sure whether we need both)
-  Examples: same as "stockbrocker\_vandalism", so we are merging them
-'stockbrocker\_vandalism'
-  Def: not quite sure how this label emerged, it does not seem to be one of the Vandalism Types in the Wikipedia Vandalism Typology %TODO: merge with 'conflict of interest'
+  Def: Filter targets people editing articles about themselves or organisations they are affilitated to or receive money from.
   Examples: 302 "Possible COI"; 588 "Promotional usernames"
 'self\_promotion'
   Def: specifically promoting one-self, it is kind of part of the 'conflict\_of\_interest'