diff --git a/trackinglosses_endVelo_momEff.ipynb b/trackinglosses_endVelo_momEff.ipynb
index f19eb5d..38d7e7e 100644
--- a/trackinglosses_endVelo_momEff.ipynb
+++ b/trackinglosses_endVelo_momEff.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -45,6 +45,8 @@
     "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
     "]  # B: 1466\n",
     "\n",
+    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
+    "\n",
     "notelectrons = allcolumns[\n",
     "    (~allcolumns.isElectron) & (allcolumns.fromB) & (~allcolumns.lost)\n",
     "]\n",
@@ -56,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,12 +70,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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3n2erY5v17/ceaPd1aPU6tuszc5T3aspBz70dOayuru6+JrcPskxPT+djY2MHfj+1+vt20Mfy8nLbrmm7zqfp6tWrBxrMOsh7e2trqyPvRQCAfpfleZ4HAAAAAADAgLJnCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgAAAAAAAAPNYAgAAAAAADDQDIYAAAAAAAADzWAIAAAAAAAw0AyGAAAAAAAAA81gCAAAAAAAMNAMhgBwz6rX67GysnLcaQAAAADQYQZDAOiIer0eMzMzMT4+HsPDw7G4uHjcKe1qNBoxPj4etVotlpeXk8dubGzE6OhoZFkWWZbFww8/HMPDwzE8PByjo6MxMzMTjUajS5kDAMBgafYbarVa1Gq1Y83FvT/AYMvyPM+POwkABku9Xo9PfvKTcfXq1YiImJmZie3t7X0HHroty7KoVquxubl5oGMjIjY3N6NarUZExNraWly4cCG2t7djfX09RkZGOpovAAAMonq9HrVa7cD35mU0Go04c+ZMDA0N7Xuse3+AwXTfcScAwOCZn5+PM2fO7P57YWHhGLNpr9s7T2NjYxERMT4+HuPj47uDPwAAwME1Bxw6aXx8PFZXVw80GNLk3h9gsFgmC4C2q9frx51C2xV1mpq/CNve3jZlHgAAetD4+Pih+iju/QEGk8EQANpmZWUlxsfHo9Fo7O7LMT4+HhsbG7vHbG9vx+TkZMzMzMTo6GiMjo7uia+trcXDDz8cWZbtdlg2NjZifHw8siyL8fHx3XpWVlaiVqvF2tpabGxsRK1W23PM7ZrtNh/t2sPkypUrbakHAAC42379hzuPmZycjOHh4VhZWYmIW/2LZr9icnLy0AMjt3PvD9DfLJMFQNtMTEzExMREDA8PR0TE6urqnnhzL5FXX311dyr8yspKjI6OxsLCQkxPT8fY2Fisr6/vdl4ibv0Cq1KpxNra2u5zV65cifX19ajX67G8vBzVajVefPHFWF5ejpWVlVhcXIzp6emIuLU+cK1Wi9XV1d1fc7VrMKSZU7VajUql0pY6AQCAg/UfIiIuXLgQlUpld3nelZWV2N7ejohby1u9/vrrsbi4GMvLy0e6Z3fvD9DfzAwBoGsuXLgQ586d27Mm8MTERFSr1ZiZmdmdat5qWvrte5BERFQqlXjmmWciInY7Q9VqdXeT9vX19d1jZ2Zm4ty5c3s2OWx2nA6r2alqNBqxuLgYMzMzUa1W49VXXy1VHwAA0NpB+w93zhSZmJhoS/vu/QEGi8EQALqi0WhEvV5vuTni5ORkRMTuQMZhtRo8aU5hbzQasba2FqOjo6XqvtP4+HgMDw/H+Ph4vP7667G8vBybm5uH2ogRAABIO0z/oVKpxOLi4p7Z32V//HQ79/4Ag8UyWQB0RWpd3nPnzkVEdGQTwmad7ZrGvr6+bko8AAB02GH6D6urq1Gr1WJmZiaWl5djdXW15SDKYbn3BxgsZoYA0FXNqea3a/6y6s6lsNqh2UGy2SEAAPSfg/QfKpVKvPnmmzEyMrK7X+DtexACQITBEAC6pPnLrDvX8434UQenufF6OzV/ybW5udn2ugEAgM44TP+h0WjE0NBQrK+vx+rqakT8aCktAGgyGAJA2125cuWumRiVSiWq1Wo0Go27lsO6dOlSDA0N7W50+IEPfCAi9i6b1fzvVr8MS2lOoV9ZWWlZ9qD1NY87bPsAAMDhHab/sLCwsBsbGxvb3UvkznLu/QHubQZDAOia1dXVGBoa2vMrre3t7VhYWIgXX3xxd7p781dgMzMzsbGxESsrK7sdmo2Njd3N0A+y9NXQ0NDu5om1Wi02Njai0WjEzMxMRNzqIN2+0eJ+LLcFAADdcdD+w8svv7xn4GN7ezsqlcruLPHmDJLl5eVoNBqxtrZ2oPbd+wMMlizP8/y4kwBgMNTr9VheXt5dn3diYiLGx8djZGRk95jt7e24cOHCbgcl4tYU9js3OFxcXIz5+fndehYWFmJ4eDjGxsbimWeeiYiICxcuRL1ej0qlEsvLy3Hu3LmYmZnZbX9hYWF3IGRlZSUWFhai0WhEtVqN1dXVGB0djbGxsZicnCzcGHFjYyMWFhZ2p+dXKpWYnJzcrRcAACjvzj7EwsJCTExM7A50HKT/MDo6Go1GI8bGxiLi1g+ebh8sibj1w6hGoxFPP/307g+t7uTeH2CwGQwBAAAAAAAG2n3HncCd1tbWYn5+fs8vfW//RXHErV8NzM/PR6VSie3t7d1f9h7XMQAAAN2k3wQAQK/b3t7eXfXj9v2dmrp9n9lTgyErKyuxubm5e2FmZmZidHQ0tra2dqdCNhqNqNVqsbm5uTslcnh4OK5cubK7cVY3jwEAAOgm/SYAAHrdxsZGLC8vx9raWst7wuO4z+ypZbIWFxf3rMNYr9ejVqvF6urq7mhPc9Pc9fX13eNWVlZicnIymqfSzWMAAAC6Sb8JAIB+kWVZTExM3LVf03HcZ544dIkOunNDquZGV81Rn+3t7djY2Ni9CE3nzp2LiFsXopvHAAAAdJt+EwAA/ey47jN7ajDkTmtra7GwsLA71fvSpUsREbv/bmre9K+vr3f1GAAAgOOm3wQAQD85rvvMntoz5HYzMzOxsrISL7744u5zjUYjIn70y6c7NRqNrh5T5Dvf+U789m//dvzUT/1U/MRP/EThcfv58R//8fjxH//x0uUBAOAgvv/978f3v//90uX/5E/+JL797W/HX/gLfyF+8id/so2ZsZ9+7TfpMwEAx23Q7oH/6I/+KL7zne8cutxRr0NExE/91E/FBz/4wQMf3+nv54v05GDI4uJiNBqN2N7ejvHx8VheXo6JiYnY2tqKiIgzZ860LLe9vd3VY4r89m//dnz6058ujAMAwCB66aWX4q/8lb9y3GncM/q536TPBAAMil64B/6jP/qjeOLRR+MHx9T+j/3Yj8Xly5fjT//pP32g4zv9/XyRQw2GXLt2LTY2NqLRaES1Wo0/9+f+3KEbPIjmGrgbGxsxPj4eCwsLMTExEcPDwxERceXKlZblKpVKV48p8lM/9VMREfFrv/Zr8dM//dOFx+2n6FdO58+fj4sXL5audz+drL9fc3/vvffiE5/4RHz5y1+OBx54oO31R7jux1F3p19X1737dXtNj6fuTtbv7+/x1e+zejz1H1fuB/012CfeKgj89/84Yv6v7d4H3+v0m/Ye00q7+0zt/Oz0Yl3tqqfdf38H+Vq1sy7X/Xjqct2Pp65eve69eK3aWZfrXq6uw8yI+N1PfOKu5/4oIn4joifugb/zne/EDyLiUxFx2DkqOz98lPXdiPitH/wgvvOd7xx4MKTT388XOfBgyPe+9704d+7cnuknw8PDsb6+Ho8++uihGz6IkZGRmJiYiMXFxYj40QkWjfpUKpWuHlOkOc37p3/6p+Nnf/ZnC48r69SpU7tro3VCJ+vv19yvXbsWEREf+chH4vTp022vP8J1P466O/26uu7dr9trejx1d7J+f3+Pr36f1eOpv+dz36dndZTljgaFftPdx7TS7j5TOz87vVhXu+pp99/fQb5W7azLdT+eulz346mrV697L16rdtblune+rv9fItZL98A/GRH/YpfbLLNgaae/ny9y4MGQmZmZ2NraipGRkRgaGop6vR6XL1+OWq1Wai2yg/roRz+6e2LNneLvXA+s+e9ardbVYwAAAG6n33T3MQAAdMd90f19Mcq0d1z3mScOemBzmvfv/u7vxssvvxyXL1+OS5cuxc2bN+Nv/+2/feiGD6rRaMTIyEhE3NospVqt3rVT/MbGRkREPP300109BgAA4Hb6TXcfAwAAtzuu+8wDD4ZUKpV47LHH9jxXrVbjxRdfjEuXLh264Ts1N/1bW1vbfa7RaMT6+nosLy/vPvfiiy/udjCaFhYWYmFhYXdn+W4eAwAA0KTf1PoYAAA6776I+LEuP1IzQ1KbnB/HfeaBZ7E8/PDDLZ8fGRmJlZWVQzd8p6Ghodje3o4LFy7E8vJyjI6ORqVSuWvkp1qtxubmZszMzESlUolGoxEzMzMxMTFxLMcAAMAgyRp5cfDPvFMQuNqRXPqRfpN+EwDQ3+ay7K7nZvO775H//t//+/F/b7GxOrfU6/XdH+u8/PLLMTo6uruUbMTx3GceeDBkdXU1PvCBD8TIyEiMjIzsbsbz0EMPxXe/+91Sjd/pzhv4ItVqNVZXV3vmGAAAgAj9JgAAiLh1f7i8vLxn9nKrY7p5n3mo/U1uT75SqeyO5gwPD7clGQ5mamqqb+vv59w7zXXvft2d5rp3v+5O85oeX/2d5Lp3v+5Oc92Pr35u0W/qvna+t3uxrl797A76tXLdu19Pu+tqp148x17Mqd0G/Vq57v1fV685Gd3fQP1kl9s7iizPW8zxaeHMmTPx9NNPx9bWVrz66qs/quCH04ZqtVo8/fTTMTIyEh/5yEciIuILX/hCfOYzn2l/1j3u7//9vx+f+MQn4stf/nL87M/+7HGnQxtcu3YtHnroofje9763++s++p/XdfB4TQeP13QweV17W3KZrOGiZbL+24j4S+5/Q7/poPSZjoe/v8fDdT8ervvxcN2Ph+veXodZJqtX7mfq9XrUarX46xHxP+1y2/8sIv7jiNjc3Ixqtdrl1g/nwANFCwsLceHChd1//8Ef/EF88YtfjI2NjajX63Hp0qXY3NyMiFvr2J47dy4uXbp0z93UAwAA9y79JgAAjktzA/Vut9kvDpzr7Tf0ERFPPfVUPPXUUxER8b3vfS82NjZifX19d3f39fX13V8/AQAA3Av0mwAAoDe1ZeDmoYceivPnz8f58+cjIuLNN9+M9fX1+JVf+ZV2VA8AAND39JsAAOgke4akdeTaPP744zExMeEXTgAA0IOyP0wE/0ziHv6+R1o/f/PhiJtHSumepN8EAHB8Wu0NEtF6fxAGw4lOVn7nFHEAAAD20m8CAIDO66f9TQAAAAAAgBZsoJ7W0ZkhAAAAAAAAx62fBm76xo//+I/v+V/63/333x+zs7Nx//33H3cqtJHXdfB4TQeP13QweV0HkftfDkef6Xj4+3s8XPfj4bofD9f9eLjux6MX72dsoJ6W5bkdYdqtXq9HrVaLzc3NqFarx50OAADskd5APREr6lnl9Ygd978cnD4TAHDcjrqBei/dzzRzmY2Ix7rc9lsRMRfRE9dhP0ceKHrrrbdieXk5Go1GnDlzJp544om4cOFCnD59uh35AQAAJTwR/7Q4+GdSIx6vFIduXCsIvHWAjO5t+k0AAMfjqIMeDI4jDYZ8/vOfj5mZmbhzcsnf/Jt/M77whS/EX/7Lf/lIyQEAAPQ7/SYAALrBBupppXN99dVXY3p6OqrVakxOTsa5c+diaGgotre34/XXX49f/uVfjscffzw+8pGPtDHd/nL+/Pk4depUy9jU1FRMTU11OSMAACjrSxHxdwti3+9mIn1FvylNnwkA6GVLS0uxtLTUMnb9+vUuZ8NRlR4MWVhYiOXl5bhw4cJdsaeeeiqefvrpeO655+KFF144UoL97OLFiz2/ThoAABzML0TExwtib8WtlYK5k35Tmj4TANDLUj/OaO7T0Uvui+7P1OinmSEnjlK41Q1909DQ0FGqBgAAGAj6TQAAcPxKD9wcZNSr0WiUrR4AAKDv6TcBANAt9gxJK53r1atX4x//438cP/3TP31X7K233orJyUm/cgIAgA76V2O9MLaVPZYo+aVErGgprIiIesHz7ybK3Nv0mwAAumcuy+56bjbPjyETelHpwZDPfe5zUalU4qMf/ejuGq/b29uxsbERjUYjhoaG4s0332xbogAAAP1GvwkAAHpD6cGQoaGh2NjYiAsXLsTCwsKeWLVajdXV1Th9+vSREwQAAOhX+k0AAHSLZbLSjpRrtVqNzc3NePPNN6Ner+8+9/jjj7clOQAAgH6n3wQAAMevLQM3jz/+eMsb+WvXrvmVEwAAQOg3AQDQWSej+zM1Tna5vaM40cnKV1ZWOlk9AABA39NvAgCAzjvwQNFHP/rRQ1der9fjl37plw5dDgAAuOWJ+KeFsa1sNFHytUTsT5Us93bB899KlLm36DcBAHTeXJa1fH42z7ucyb3htyLi/1MQ+343EzmiAw+G5Hm+u77tQWUFb0oAAIBBpN8EAMBx6dQG6p/64aOVr0fE/7kDbXbCgZfJGhkZic3Nzbh58+aBHxcuXOhk7gAAAD1FvwkAAHrTgWeGPPPMM/HUU08dqvLJyclDJwQAANCv9JsAADguNlBPO/DMkKIb+mvXrsXv/d7v7f771Vdf3f33YTsBAAAA/Uy/CQAAetOBB0NaefbZZ+Phhx+OP//n//zuc5/85Cdja2srnnvuuSMnBwAA0O/0mwAA6IbmniHdfHR7JspRlM71V37lV2J5eTmGhobu2vDvwoUL8ef//J+Pv/23/3b81b/6V4+cJAAADLKfvPnNwth3T/6ZRMlXErHLidjZROyRRIzD0m8CAChv4477p6bZPO9yJgyC0jND1tbWYm1tLa5cuRKf/OQn74qPjo7G5z73uSMlBwAA0M/0mwAAoDeUnhlSqVTiU5/6VETEXb9wioh4/fXXo9FolM8MAACgz+k3AQDQLTZQTys9M2RoaGj3v/M7piX9wR/8QaytrUWlUimdGAAAQL/TbwIAgN5QeqDoueeei5/7uZ+LhYWF3V84vfXWW7G2thYzMzORZVlMTk62LdF+dP78+Th16lTL2NTUVExNTXU5IwAAKOu3IuI3C2I/6GYifUW/KU2fCQDoZUtLS7G0tNQydv369S5ns7/mBurdbrNfHDjXr371q/GRj3xk999PPfVUzM/Px2c+85mo1+uxtrYWET/6tdPMzEz80i/9Unuz7TMXL16MarV63GkAAEAb/GsR8WRB7I8iwr4XEfpNh6XPBAD0stSPM+r1etRqtS5nxFEceDDkwoUL8frrr+95rlqtxqVLl+LNN9+Mzc3NePPNN6NSqcTIyEg89NBDbU8WAAD61V+M1cLYd39yPFHy6yVb/PlE7JuJ2Fsl2yNCvwkAoKy5Fvurzd6xzCgcxYEHQzY3N+MDH/hAPPfcczExMRGnT5/ejT3++OPx+OOPdyRBAACAfqHfBADAcbkvur9sVT8tk3XgDdSr1Wp87nOfi8uXL8djjz0WzzzzTPze7/1eJ3MDAADoK/pNAADQmw48GPLcc8/FhQsX4td//dfjypUr8fTTT8f09HR84AMfiM9//vNx7dq1TuYJAADQ8/SbAAA4Ls0N1Lv5GMiZIefPn7/r35cuXYrXX39991dPP/dzPxe/+Zu/2fYkAQAA+oF+EwAA9KYDD4YUqVQqu796mpiYiL/6V/9qfOADH4i/8Tf+Rrz11lttSBEAAKC/6TcBANBpJ+NH+4Z063GyK2fWHm2bxfKFL3whFhYW4nvf+17keR6f+9zn4urVq/HCCy+0qwkAAIC+pt8EAADH40iDIdeuXYv5+flYWVmJ7e3tyPM8hoaG4rnnnouJiYl46KGH2pUnAAD0vL8Yq4Wx38rGS9a6kYh9PBGrJ2LvJGJPpNNp6Y9LlLl36DcBAPzIXJa1fH42z7ucCfeaAw+GvPLKK/GpT30qIiK++tWvxvz8fKytrUVERJ7nUalUYmZmJi5cuNCZTAEAAHqcfhMAAMeluYF6t9vsFwfOdX5+Pq5cuRKrq6uxsbER+Q9H6kZGRmJmZiY++clPdixJAACAfqDfBAAAvenAgyGbm5sxOTm5ezM/NjYWCwsL8fjjj3csOQAAgH6i3wQAwHExMyTtxGEOzvM8pqen4+rVq/Hyyy+7oQcAALiDfhMAAPSeAw/cVCqVqNfrcfr06U7mAwAA0Lf0mwAAoDcdeDBkeXnZDT0AAPe8yfhPCmO/lf27xQUfTlR69bVE8LFE7HIidj0RezARO1UYyfNfaPl8vV6PWi1R5T1EvwkA4Ja5LGv5/OwPlxOl/U5G95etOtnl9o7iwMtkHXajv89//vPx1ltvHTYfAACAvqXfBAAAvelAA0UvvvhirKysHLjS7e3taDQaceXKlfibf/Nvlk4OAACgX+g3AQBwnO47GfFjrSfkdK7NPCJ2uttmWQcaDDl37lxMTk4euvLV1VU39QAAwD1BvwkAAHrXgZbJeuqpp2JsbCxu3ry5+1hYWIiFhYU9z93+mJ6ejvX19U7nDwAA0BP0mwAAOE4nT0bcd193Hyf7aNOQA+8ZsrCwsOffjUYjfvmXf7nw+MnJyRgfHy+fGQAAQJ/RbwIAgN504M3lH3/88UNV3Gg0ol6vHzqhQXL+/Pk4depUy9jU1FRMTU11OSMAAA7iX43iX+r/gx/7dxMl3y4OXf1SotwjidhbiVjKxxOx1H160Tm8Gln2mYLY9w+W0j1Av+lw9JkAYDDMZXdvVDGb58eQSXstLS3F0tJSy9j169e7nA1HdeDBkDvleR7/9X/9X8ef/bN/9q7YtWvXYnJyMiqVypGS63cXL16MarV63GkAAEAbfPKHj1beioi57qXSR/Sb0vSZAIBelvpxRr1ej1qt1uWM0u47EfFjHVi26td/ELHyg9axP+mjMa/SgyGf+9znolKpxEc/+tEYHR2NSqUSV65cic3NzVhZWYmIiOXl5bYlCgAA0G/0mwAA6Hf/zo/derTyBzsRH/+T7uZTVunBkKGhobh06VLMzMzE9PR0ZD+cCpX/cPrT9PR0fOYzRdPoAQAABp9+EwAA3XLffRH3dXlD8/vuXiGtZ5UeDImIqFQqsbq6Gm+++WZsbW3Fm2++GZVKJc6dOxcPPfRQu3IEAADoW/pNAABw/I40GNL0+OOPt9wo8POf/3z80i/9UjuaAAAA6Gv6TQAAcHyONBjy+c9/PtbX1+PKlSst4/V63U09AAA96S/GamHsH2TjiZIvJWKfSsROJ2LXE7Fy8vzDhbEs+81EybOJOp9t+fytzSNtoF5EvwkAGBRzWes1kWbzPtpFe4DddzLix9oy/eEQbXa3uSMpneszzzwTq6vFHciI2F0PFwAA4F6k3wQAAL3hRNmCq6urMTk5GTdv3ix8XLhwoZ25AgAA9BX9JgAAuuZERJzs8qP0CEP3lU61Wq3G5ORk8piFhYWy1QMAAPQ9/SYAAOgNpQdDFhYW4otf/GLymM3NzbLVAwAA9D39JgAAuuZk3NoYo5uPk105s7YovWfI9vZ21Ov1+PznPx9DQ0Mtj1lYWIivf/3rZZsAAADoa/pNAADQG0oPhszPz0e9Xo/19fXCY2wECADAcfo78Uxh7Lf+xdSv9d9OxE4nYq/tl1KBa4nYu4nY2cJIli0myj2ZiL2RqHOuIPKtRH33Nv0mAKBfzbW4R5nN82PIBNqj9GDIxMREbGxsxDPPtO5gfve7342VlZXSiQEAAPQ7/SYAALqmuXRVN93scntHUPrSPPPMMzE6OhqPP/544TEf/ehHy1YPAADQ9/SbAACgN5QeDHnooYfioYceuuv5t956K86cOROnT5+Op5566kjJAQAA9DP9JgAAuqa5gXo37XS5vSM48KX5vd/7vd3//nN/7s8VHnf16tUYGxuL733vezE6Ohp/62/9raNlCAAA0Cf0mwAAoDedOOiBIyMjMTMzE9vb2xERce3atbseERFPPfVUXLp0KR577LFYXl7uSNIAAAC9SL8JAAB606Emzbz++uu7/72+vh7z8/PxB3/wB1GpVGJycjJ+6Zd+aTe+vLwcH/zgB9uXKQAAtPBvx4uFsd/4mS8WF/z2tUStryRipxKxy4nYg4lYSqrcOyXLpc4hFbueiNGk3wQA9JO5LGv5/GyedzkTjuxE3Foqq9tt9okDD4ZUKpU9/z5//nycP38+Tpw4Eevr6/HYY4/ddXxqk0AAAIBBo98EAAC96cCDIQ8//HDL5yuVyl039PuVuVecP38+Tp1q/cu6qampmJqa6nJGAABQ1j+MiNcKYje6mUhP0286HH0mAKCXLS0txdLSUsvY9es9OGv6ODZQ7/ZMlCM48qXJCqZREXHx4sWoVqvHnQYAALTBz0TEUwWxb0XEShdz6T/6Ta3pMwEAvSz144x6vR61Wq3LGXEUfbSiFwAAAAAAwOEdeGZIo9GIb3zjG5G32Din1fNXr16Ner1+9AwBAAD6hH4TAADH5r7o/jJZ3W7vCA6c6tWrV+/aDLCp6HkAAIB7iX4TAAD0pkON27T6dVOKdXEBAGiHJ+P1wtg/OXOhuODVa4la//NE7MOJ2NcSsZTWm0R3zmOJ2OlE7Gwi9k7B8z+xbzb3Ev0mAKAXzRXcc8we8t6FHnYiur+heR9txHHgVKvVaty8efNQj09+8pOdzB0AAKCn6DcBAEBvOvDMkOeee+7QlU9OTh66DAAAQL/SbwIA4NicjO7v4dHtmShHcOCZIefPnz905WXKAAAA9Cv9JgAA6E19tKIXAAAAAADA4XV70gwAAAAAANBu90X3v/HvoxGGPkoVAIBBNhe/Uhj7J9lfL1nrG4nYI4nY10q292Ai9k4idjYRu56IvZuIfSURO5WIFcvz6ZbP1+v1qNV+rVSdAAC031yW3fXcbJ4fQybQOwyGAAAAAABAvzsRHdnQfOnbtx6tXL/Z/vY6xWAIAAAAAADQ0tRP3Xq0Un8vovaPu5tPWQOzgXqj0TjuFAAAAHqafhMAAPeqnhsMWVtbi1qtFlmWRa1Wi42NjZbHZVm25zE+Pr4nXq/XY3x8PGZmZmJycjLW1tbuqqNdxwAAAHSTfhMAAHc5GT/aRL1bjw4sy9UpPbVM1uLiYqyvr8fk5GRsbW3F4uJijI6Oxvr6eoyMjOwet7KyEhMTEzE8PLz73O3xRqMRtVotNjc3o1qtRkTE8PBwXLlyJSYmJtp6DAAAQDfpNwEAwOFleZ7nx51E0/j4eKyuru7+u16vR61Wi5GRkVhfX999vnmjX2R0dDQiYs8xKysrMTk5Gc3TbdcxrTTzvr0zAABwr/tc/PVk/Ll/8z8uDv4XryVKvl0y9kgyn3J1vpuIPViyvXdKljtbGMnzZ0vVmGVzBZFvRcSK+98uGYR+kz4TALTHXJa1fH62d77yHVi9dD+zm8tHI6qnu9z2tYja69ET12E/PbNM1sbGRiwsLOx5rlqtRrVa3bOu7draWly6dCnGx8djZWXlrnq2t7djY2Nj94a86dy5cxFx66a8XccAAAB0k34TAACU0zODISMjI1GpVFrGbn9+fX09tre3Y21tLSYnJ+Phhx/esz7upUuX7ioTEbujUuvr6207BgAAoJv0mwAAKNTt/UKajz7R86k2Go2YnJzc/ffy8nIsLy9HvV6P5eXlWFlZidHR0dja2opKpbL7a6ihoaHC+tp1zH7ee++9uHbt2r7HFbn//vvj/vvvL10eAAAO4v3334/333//AEf+ScHz329nOpTQr/0mfSYA4Lgc/B64tffee6+N2dANPT0Ysra2FpVKpeWme9VqNZaXl2N0dDTGx8djZmYmVldXY2trKyIizpw507LO7e3tth2zn0984hP7HpMyOzsbzz///JHqAACA/czPz8fcXNF+IPS6fu436TMBAMfFPfC9p6cHQ+bn5/dsDNjK2NhYjI2NRb1ej4iI4eHhiIi4cuVKy+MrlUrbjtnPl7/85fjIRz6y73FF/MIJAIBueO655+Kzn/3svsc99NB8QeR/iIiX2pkSh9DP/SZ9JgDguBz0HrjIV7/61SP/sKPtTkTEyWNos0/07GDIzMxMvPjiiwcadBgdHd1d/7Z5fNEvkCqVStuO2c8DDzwQp0+f3vc4AIBB8Uz8ncLYy9l/vE/ptxOxr5XKJ+JUIvZEIvZaIvZuIvZgyXJnE7Gy3imMZNliotz1ROwnCp7/8YMkRAf0e79JnwkADmYuy1o+P5vnXc5kcBx1uc0HHnigjdnQDT05GNJcz7a56d5BnDt3bs//3rk2bfPftVqtbccAAAAcF/0mAAD2OBnd/8a/2zNRjqDnJrGsra1FRMTIyMie55vTuVtZX1/f3SxwaGgoqtVqrK+v7zmm+Quop59+um3HAAAAHAf9JgAAOJyemhmysbER8/PzMTk5GSsrK7vPb25u7v6i6MKFC/HMM8/E9PR0RNzqBJw5cybGxsZ2j3/xxRejVqtFo9HYnZa9sLAQCwsLMTQ01NZjAAAAukm/CQAADq9nBkPq9XqMjo5GROz+Wul2V69ejYiIM2fOxPz8fKyvr0e1Wo3R0dFYXl7ec2y1Wo3Nzc2YmZmJSqUSjUYjZmZmYmJiou3HAAAAdIt+EwAAhSyTldQzgyHVajXyA2z4c+f061R9q6urXTkGAACgG/SbAACgnJ4ZDAEAoD/82/FiYezln7mQKHltn5qL9zpIeyQRO5WIfbNkuXcSsbMly10vWee7perM8+nCWJYtlqoTAID2mMuyu56bPcCPISBORvdnavTRzJCe20AdAAAAAACgncwMAQAAAACAfmfPkCQzQwAAAAAAgIFmMAQAAAAAABholskCAAAAAIB+Z5msJIMhAADc5a/H5wpjv3HmV4oL/kSq1tf2afV6IvZuInYqEbtcsr2zpdrL808XxrJsLlHnO4lYsTyfLdleyhOJ2NsFz/9JybYAAO5dc1nW8vnZPO9yJnBvMBgCAAAAAAD97mR0f6aGmSEAAAAAAMC9am1tLdbX12NoaCgajUZUKpVYWFjYc0y9Xo/5+fmoVCqxvb0do6OjMTY21pF8DIYAAAAAAABts7a2FvPz87G5ubn73OjoaMzMzOwOiDQajajVarG5uRnVajUiIoaHh+PKlSsxMTHR9pxOtL1GAAAAAACgu5obqHfzUbBM1vLycpw7d27Pc6Ojo7G2trb778nJyRgZGdkdCImImJmZicnJybJXIMlgCAAAAAAA0DZXrlyJjY2NPc9tbW1FpVKJiIjt7e3Y2NiI0dHRPcc0B1BWVlbanpPBEAAAAAAA6Hc9NDNkcnIyGo1GjI+PR8StvUFefvnl3SWyLl26FBGxOzjS1Jwlsr6+Xv46FLBnSAedP38+Tp061TI2NTUVU1NTXc4IAOBH/np8rjD2q9n/MVHyWiL2dul80vU+mIh9IxH7WCL2RiL24URsozCSZXOJcuXk+WyivcVEydb3obfKlcnzKz98tHKjRH2gzwTAvWEuy1o+P5vnXc6Ew1paWoqlpaWWsevXr3c5m854f+fWo6z3ftD6+YmJidjc3IyVlZUYHh6OSqUSb775ZgwNDUXErf1CImL333dqxtvJYEgHXbx4cc96ZwAA0L8+FsWDS9+KiPZPY2fw6TMBAL0s9eOMer0etVqtyxntozkz5BDmNyPmXu9INrG8vByXLl2Ker0ejUYjNjY2YmxsLCJuLZkVEXHmzJmWZbe3t9uej8EQAAAAAAC4Bz13LuKzT5Uv/9XvRHzildax0dHRmJycjEqlEuPj4zE+Ph6rq6sxNjYWw8PDEXFrb5FW7lw+qx0MhgAAAAAAwD3o/pO3HmU9UDDCMDk5GRG3lsuKiHjzzTfj8ccfjwsXLsTY2NiejdRb6cRgiA3UAQAAAACg3508pkcLL7/88p7lUIeGhmJhYSG2t7ejXq/HuXPnIuLuvUGa/+7EEmQGQwAAAAAAgLY5c+bMXbM+RkZGIuLWwMjQ0FBUq9VYX1/fc8zGxkZERDz99NNtz8kyWQAAA+wvxmph7Ld+7FdK1vqlROx6Ivb2PvU+UrLeU4WRPP+FwliWvZWo8+uJ2NlE7LFELNVe8TlkWcECvBGR59OJOtsvyxYLIj/R1TwAAHrVXJbd9dxsnh9DJtyTSmyg3pY2W5icnIz5+flYWFiIoaGhiIhYW1uLarW6uwTWiy++GLVaLRqNxu5zCwsLe8q0k8EQAAAAAACgbaanp2NoaCjGx8d3l8va3t6OV199dfeYarUam5ubMTMzE5VKJRqNRszMzOzuM9JuBkMAAAAAAIC2mpiY2Hdgo1qtxupq8YoG7WQwBAAAAAAA+l0PLZPVi2ygDgAAAAAADDQzQwAAAAAAoN+djO7P1OijmSEGQwAA+t0/ygpDv3UhLy5347WSDb6ViD2SiL2zT72nErEPJ2LF55Flr5TM58lELOXtkuWuJ2LfKIxk2bWSde73WrSW57Mtn6/X61Gr/VqpOgEA+tFc1voefDZP3H8Dx8pgCAAAAAAA9Dt7hiTZMwQAAAAAABhoBkMAAAAAAICBZpksAAAAAADod5bJSjIzBAAAAAAAGGhmhgAA9IG/E88Uxj79v8pL1vpEIvZKIjaSiG0kYk+m00n6emEkz6cLY1n2Qsn2riVi1UTstZLtnSqM5PlsqRqzbC4RPZuIvVOqPQCAQTWXZXc9N5uXvQeHDjIzJMnMEAAAAAAAYKCZGdJB58+fj1OnWv/Kb2pqKqamprqcEQAAlPWV+NCHPtQycv369S7nwqDQZwIAetnS0lIsLS21jLkH7j8GQzro4sWLUa2mllEAAIB+8bH4wz/87ZaRer0etVqty/kwCPSZAIBelvpxRk/eA5+M7i9bZZksAAAAAACA3mBmCAAAAAAA9DsbqCcZDAEA6BX/KCsMffq1vGSlX0vEHitZ528mYg8mYpf3qffnE7HfKYxk2UuJcu/s02aR06VyyfPpUq1l2Vwitpgo+UQidrYwkufPJtp7qUQu30zkAQDQH+ay1vfjs3nZe3GglxgMAQAAAACAfmdmSJI9QwAAAAAAgIFmMAQAAAAAABholskCAAAAAIB+dzK6v2yVZbIAAAAAAAB6g5khAABd9MfvF/9s5oF/Ky8u+N1UrV9LxOqJ2OVE7JGSsWuFkTx/NlEuIsteSkTPJmLfSMROJWLXE7G3E7FiWbaYiKbOoTiWum5Z9kLJcqk8i+X5dMvn6/V61Gq/VqpOAIBum8uyls/P5on7cegHNlBPMjMEAAAAAAAYaGaGAAAAAAAALS39N7cerVz/QTczORqDIQAAAAAA0O86tEzW1MitRyv1b0TU/v32t9kJlskCAAAAAAAGmpkhAAAAAADQ705E9zc076PpFgZDAADa7OfivyqM/e5Hd4oLfjdR6dWXEsHTidgjiVhKNRH7UqlcsixVLiLieiL2biJ2NhF7p1S5PH+2MJZli4k6H0zEUudXLN1euTrzfLpkLnMFkW+Vqg8AoNPmsuyu52bz/BgyAY5bH43bAAAAAAAAHJ6ZIQAAAAAA0O/ui+5/499HIwxmhgAAAAAAAAOtj8ZtAAAAAACAlk5G97/x7/aG7UdgMKSDzp8/H6dOnWoZm5qaiqmpqS5nBAAA5SwtLUXEf1YQvdHNVBgg+kwAQC9bWlr64X3w3a5fv97lbDgqgyEddPHixahWq8edBgAAHNnU1FT84i9+pyD6rYhY6WY6DAh9JgCgl6V+nFGv16NWq3U5o32YGZJkMAQAoIT/Kn6uMPa7Z/5eccGrXy/Z4gcTsbdLxlK/ZEqVS+WykYg9mohFRLybiD1YqlyezxbGsmyuVKy84uudyjMly14oWW4xET18nrc6ggZDAIDjM5dlLZ+fzfMuZwL0KhuoAwAAAAAAA83MEAAAAAAA6HcnovvLVvXRdIs+ShUAAAAAAODwzAwBAAAAAIB+d190/xv/PhphMDMEAAAAAAAYaH00bgMA0F2vx5OFsX/9b7xRXPDq1xO1JsrF24nYSCL2WiJWfA4Rp0vW+Y1E7GzJchERjxZG8vzThbEseyERm9unzSKnErlMl2yvuM6ULHspkcuzJXMBAOhPc1nW8vnZPO9yJkC/MRgCAAAAAAD97mR0/xv/bm/YfgSWyQIAAAAAAAaamSEAAAAAANDvTkT3Z2r00XSLPkoVAAAAAADg8MwMAQAAAACAfmfPkCSDIQDAPe2P3y++c/vY4zvFBb+dqvWNROxaIvbxROw3Uw0mnE7EXkvEHkzEziZi30jETiVi6bJZ9sI+Zcu0eb1UjVk2l4imrs07pdpLX9PuKj73b3U1DwDg3jCXZXc9N5vnx5AJMAgskwUAAAAAAAw0M0MAAAAAAKDf3Rfd/8a/j0YYzAwBAAAAAAAGWh+N2wAAAAAAAC2diO5vaN5H0y36KFUAAAAAAIDDMzOkg86fPx+nTp1qGZuamoqpqakuZwQA96gvZYWhB76UF5f79tdKNvhIIvbxROyVROx6IvZkItb+c8jzTxXGsmwxUWfqHCIiWt833fJuIp/pRD4v7NPm4eX5bKK9uUTJ4vNLlSvf3tlE7J1Dt7e0tBS/+Iv/XkGpHyTagmL6TABERMxlre/XZ/PEvTp0wdLSUiwtLbWMXb++X/+GXmMwpIMuXrwY1Wr1uNMAAIAjm5qail/8xT8uiH4zIn6tm+kwIPSZAIBelvpxRr1ej1qt1uWM9nEyuv+Nf7eX5ToCgyEAAAAAAEBLS2u3Hq1cf7+7uRyFwRAAAAAAAOh390VHvvGf+jduPVqp//cRtX+r/W12gg3UAQAAAACAgWZmCAAAAAAA9Dt7hiQZDAEABsPlrDCU/ft5yUo/XBxK3UXdeCURfDIReyQReywRO52IXU7EUt4ujGTZl0rWebZkuYg8f7YwlmVziXKziXKLpXLJshdKtlecZ7q9cnlGvFMYaX+ef1KiDABwr5nLWt+zz+Zl79cBDs4yWQAAAAAAwEAzMwQAAAAAAPrdiej+slV9NN2ij1IFAAAAAAA4PDNDAAAAAACg39lAPcnMEAAAAAAAYKCZGQIA9I9/lBWGsr+XF5d7PVHnja8lgt9MlLueKPd2IvZKIvbBROzriVgql1TsVCL2bmEkz3+hMJZlb5Sq85Yn9okXOVsYybLFwlieT5cqF/HOQZJqIXW9Ux5MxFKvb1mPJmLf6EB7AMAgmsvuvnefzRP37AAdZjAEAAAAAAD63X3R/W/8+2iEwTJZAAAAAADAQOu5cZu1tbWYn5+Per0e1Wo1FhYWYmRkZM8x9Xo95ufno1KpxPb2doyOjsbY2NixHQMAANBN+k0AANzlRHR/Q/M+mm7RU4Mhi4uLsb6+HpOTk7G1tRWLi4sxOjoa6+vruzf2jUYjarVabG5uRrVajYiI4eHhuHLlSkxMTHT9GAAAgG7SbwIAgMPrqXGb119/PdbX12NiYiIWFhZic3MzIiIWFhZ2j5mcnIyRkZHdm+yIiJmZmZicnDyWYwAAALpJvwkAgJZOxo/2DenWo9szUY6gZwZDNjY29ty8R0RUq9WoVqvRaDQiImJ7ezs2NjZidHR0z3Hnzp2LiIiVlZWuHgMAANBN+k0AAFBOzyyTdef6trerVCoREXHp0qU9/25q/gJpfX19N9aNY0z5BoAOuJwVhrL/PC8u95++XbLBDydi9UTsg4nYtUTsnZKxs4nY5UTsVCKWcr0wkmWLhbE8n06Ue2GfNouvW6rNtAdL1ll8/unXIiVVruz7ovj1Tb0WaWXa+4mSbXFY+k0A9Jq5rPX9+2yeuHcHOAY9MxhSpNFo7E6vbv7SaWhoqPDYbh6zn/feey+uXUt9GZJ2//33x/3331+6PAAAHMT7778f77///g//9SdlamhnOpTQr/0mfSYA4LjsvQc+vPfee6+N2bRJc5msbrfZJ3p6MGRtbS0qlcruL4m2trYiIuLMmTMtj9/e3u7qMfv5xCc+se8xKbOzs/H8888fqQ4AANjP/Px8zM3NHXcalNTP/SZ9JgDguLgHvvf09GDI/Px8rK6u7v57eHg4IiKuXLnS8vhKpdLVY/bz5S9/OT7ykY/se1wRv3ACAKAbnnvuufjsZz8bEREPPfSrJWr4VkQstzMlDqGf+036TADAcbn9HriMr371q0f+YUfbNTc173abfaJnU52ZmYkXX3xxz81z87+Lfl1UqVS6esx+HnjggTh9+vS+xwEAwHHau9RQmf0/fCF9XPq936TPBAAcl6Mut/nAAw+0MRu64cRxJ9DKyspKjI6O7m6613Tu3LmIuHvd2ea/a7VaV48BAAA4LvpNAABwcD03M2RtbS0iIkZGRvY8X6/Xo1qtRrVajfX19Zient6NbWxsRETE008/HUNDQ107BgAo54/fL95h7YGP5cUF/+cdSCapmojVE7FHErHUL6AvlyyXau+NROxsIna9MJLnzxbGsuyVRJ2pPCNS55/n04WxLFtMlCvONSXLyq0dnC53qlSdqXKp65KSZS+UqrP4/Mpsus5R6DcBcBzmsuyu52bzxP070FX5iYi8yxua5z053aK1nhoM2djYiPn5+ZicnIyVlZXd5zc3N6NWq0W1Wo0XX3wxarVaNBqN3SnXCwsLsbCwEENDQxERXT0GAACgm/SbAADg8HpmMKRer8fo6GhERExOTt4Vv3r1akREVKvV2NzcjJmZmahUKtFoNGJmZiYmJiZ2j+3mMQAAAN2i3wQAQJGdkxE7Xf7Gf6fLM1GOomcGQ6rVauQHnFZXrVZjdXW1Z44BAADoBv0mAAAop2cGQwAAAAAAgHJuHsPMkJt9NDOkj7Y3AQAAAAAAODwzQwCAjvjj94t/HvLAV3eKC169Vhz7/dcSLZ5OxM4mYvVE7K1E7FQidj0Ru5yIPZiIJa5Lss6UdwojeT5dGMuyxZLt7Sd1/inF1zuda+p1Sr2+xdctJX1N50rVWVaeP1sYS+WS57Mtn6/X61GrrbSMAQD9Zy7LWj4/e8ClGgF6kcEQAAAAAADoczsns7hxsvVgZufazCOiPwZKDYYAAAAAAAAtLf96Hiu/3nrA40/+pMvJHIHBEAAAAAAA6HM7J0/Gzn3t3yb8M79469HKV+s34xMfu9H2NjvBBuoAAAAAAMBAMxgCAAAAAAAMNMtkAQCl/fH7JwtjD/wvdooLXv56otZHyidU6LWS7Z1KxK4nYqcTsQcTsXdLxs4mYqk8i+vMsrlEudR1KZbn08l4lr2UiC0m6p0tVS7iyUSdn0rUWe7apHMpVvb8Utc7y15ItFju9QUA+s9c1nqj5dm8PzZDBva6efJk7Jzs7vyHmyeziLBMFgAAAAAAwLEzMwQAAAAAAPrcTpyInShewaEzbfYPM0MAAAAAAICBZmZIB50/fz5OnWq95vLU1FRMTU11OSMAACjrH8aHPvShlpHr11N700AxfSYAoJctLS3F0tJSy1gv3gPvxMm4YWZIIYMhHXTx4sWoVqvHnQYAALTBz8Qf/uF/2TJSr9ejVqt1Nx0Ggj4TANDLUj/OcA/cfwyGAABpy1lh6IHn8uJyfylR5+VUg6lf1/xCIva1wkief7owlmVziTo/loi9kYilfDgR20jEnkzE3k7EWv/i+pZ3E7GU1Gt0tjCSZYsl24uIeDBR7wuJcqlci9+I6fdF6pqmpHIprjN13fJ8OlEudQ7lFOfyzba3BQC011x29339bJ64nwcYMAZDAAAAAACgz92Mk7HT5a/8b3a1taOxgToAAAAAADDQzAwBAAAAAIA+txMnYqfrG6j3z9wQM0MAAAAAAICBZjAEAAAAAAAYaJbJAoB73VqWDGd/nBcHr369OPY7H0zU+kgitpGIXUvE3imMZNm7iXIfS8SeKIzk+S8k2vtSos7XErFTidgbJculzv3BRKz43FO55PmzhbEsm0vUGZE+j+LXN+JsyViqzuJc8nw6Ua6c/a/N4eX5bMn2Hi3R2v9YogwA0AlzWev7+9k8cV8PDIRbG6h3d5msm5bJAgAAAAAA6A1mhgAAAAAAQJ+7eQwbqN+Mna62dxRmhgAAAAAAAAPNYAgAAAAAADDQLJMFAAAAAAB97kaciBtdXibrRh/Nt+ifTAEAAAAAAEowMwQA7gUjWWEo+9k8XfZXU8HTxaFvv5Ao91gidjkRezARezIRezsRS3mtMJJlb5Rs72zJXD6YiH0tEXu3MJLnzxbGsmwxUWfxOWTZXKLcfq6XajN1jqk683y2MJY6j1QsVWdKt3Mp215RuXq9HrXaUV57AOCw5rLW9/iz+T7398DAuhn3xU6Xv/K3gToAAAAAAECPMDMEAAAAAAD63M04ETtd3jPk5iHmWzQajVhbW4uIiImJiRgaGoqIWzPN5+fno1KpxPb2doyOjsbY2FjbczUYAgAAAAAAdESj0YiZmZnY3t6O5eXlqFQqe2K1Wi02NzejWq1GRMTw8HBcuXIlJiYm2pqHZbIAAAAAAIC2u7W/YC3OnDkT6+vrewZCIiImJydjZGRkdyAkImJmZiYmJyfbnovBEAAAAAAA6HM7P1wmq7uP4iGG7e3t+OQnPxmVSiWWl5dbxjc2NmJ0dHTP8+fOnYuIiJWVlbZeH8tkddD58+fj1KlTLWNTU1MxNTXV5YwAGGi/lxWGsj+dF5f7nX3qvZoKtv7/uVs+vE/FRZ5IxK4lYm8nYo8kYm+k0ylUTcQuJ2LvFkbyfLowlmUv7ZvRYWXZYslcistFnE3E3tk/qcJ8ni1VLsvmSsXS7+3riTrLXdOUPJ8t1V5K6tyL2ltaWooPfehDLWPXrxdfE0jRZwI4mLns7nv92Txxjw+0xdLSUiwtLbWMuQfeX3NprIWFhZbxS5cuRUTcNVukOUtkfX29rUtlGQzpoIsXL+6Z3gMAAP0q9cV0c+o7HJY+EwDQy/rtHngnTsaNQ26g/v33b8b33y8/uPrue8Wx5syO9fX1mJmZiUajEefOndvdN6TRaERE7G6kfqdmvF0MhgAAAAAAwD3opfl/Hl+Y++dtr7der0fErVkek5OTsbCwEI1GI0ZHR2N4eDiuXr0aW1tbERFx5syZlnVsb2+3NSeDIQAAAAAAcA/69HP/Qvybn/3J0uX/v1+9Hv/OJ9666/nmrI7JycndZbCae4eMjo7G/Px8DA8PR0TElStXWtZ95/JZR2UwBAAAAAAA+tzNOBk7h/zK/+T9EafuL9/mTzyw0/L5oqWvRkZGIiJ2Z4lEFM8AafdgSPFW7wAAAAAAAId07ty5iIjdpbDudObMmd1j7twbpPnvdu/JYmYIAPSTv5AVhrL/ZWLDs+8k6vz9/Rp9OxG7nIhdS8ROl6ozz6cLY1n2Ssn2iqXbWyxVZ8TZRJ1ziXKPJmLvlszliUQuqfO7nog9WBjJ89lkNqnzL3+9T5UslzrHVJ2pcuWk3xfl7PdaAADHYy5rfb8/m5ff3Bi4d+zEydg55Abq7WizlaGhoRgZGYmNjY09zzdngdRqtRgaGopqtRrr6+sxPf2j/nezzNNPP93WXM0MAQAAAAAA2mphYSHq9fqeAZGVlZWoVqsxMTEREREvvvhibGxs7JkdsrCwEAsLC4VLbZVlZggAAAAAAPS5m3Gi6zNDbibmW1Sr1djc3IyZmZlYXV2NoaGh2N7ejs3NzZbHVCqVaDQaMTMzsztY0k4GQwAAAAAAgLZrLoO13zGrq6sdz8UyWQAAAAAAwEAzMwQAAAAAAPrczjEsk7XTR/MtDIYAQK95NisMZf/pzeJyP5eo83KqwRf2Seh/n4g9mYhtJGK/kIh9vTCSZa8kyp1KxL6WiD2YaO+lRLmUJxKx1IvxaCL2TgdyeSMRO1uyvXcLI1m2mCyZ57OJsnOJksW55vmzyTbLyLLiz0yeTyfKpc6h+P2bui4pqfbK11n0Gn6zVH0AcK+ay1rf88/meZczAbh3GAwBAAAAAIA+txMn40bXZ4Z0t72j6J85LAAAAAAAACUYDAEAAAAAAAaaZbIAAAAAAKDP3YyTsdPlr/xvWiYLAAAAAACgN5gZAgDH4V/JCkPZv5wXlzuXqPPqtZLJfHyf+NuJ2AcTsVQ+X0/EHknE3krE3k3EridiKWcTsQcLI3n+qVKtZdliqXJ5Pp2o84VEyUcTsW8kYqcSseJrneeziXIRWTZXss3Ua59qr/3XO12u+PzT595dqVyKzqFer0et9mudSgkA+tpcdve9/2yeuOcHKGknTnR9Q/OdPppv0T+ZAgAAAAAAlGBmSAedP38+Tp1q/SvGqampmJqa6nJGAABQ1lfiQx/6UMvI9etlZ19xr9NnAgB62dLSUiwtLbWM9eI98K09Q7o7M6Sf9gwxGNJBFy9ejGq1etxpAABAG3ws/vAPf7tl5NYyWbUu58Mg0GcCAHpZ6scZ7oH7j2WyAAAAAACAgWZmCAAAAAAA9LmdOBE3bKBeyGAIAHTKfFYYyrK8uNxvlG3wdCL2diL2zX3qfSIReyURS62fWjbXlFSe1xKxVJ7vlsoky+YS0UdL1Vm+vZSzhZE8ny3VXqrc0fJ5NpHPYqnW8nw6UWe5a9q5a3P49tLlyl2z4va+Vao+ABgkc1nr+//ZPHHvD0DXGAwBAAAAAIA+txMnY6fLX/l3e8P2o+ifOSwAAAAAAAAlGAwBAAAAAAAGmmWyAAAAAACgz92Mk11ftuqmZbIAAAAAAAB6g5khAAAAAADQ53biRNdnhuz00XwLgyEAcARfz7LC2P/sX8mLC/7+tUStpxOxVLmNRCzlkX3iv5mIPZmIPXb4VPb1TslYyqlE7Hqpcnk+WxjLsrlEnWcTdT5bqs6yuZTNM8sWE+X2k7re5WTZC4Wx1DVN19n+651ur/iapttLvRYPJmLFr0NRe/V6PWq1lUSdADBY5lr0A2bzxP0/AMfOYAgAAAAAAPS5nTgZN7o+M6R/9gwxGAIAAAAAALS0sfT1+L2lrZax71/f6XI25RkMAQAAAAAAWhqZ+mCMTH2wZeyt+tV4vrbe5YzKMRgCAAAAAAB97macjJ0uf+V/s4+Wyeqfrd4BAAAAAABKMDMEAPYxl2WFsefn8uKCa6la3y4Zu5yInUrEridiDyZi+8X/VCL2O4lYKp9U7MlELHX+qWta9toUt5dlc4lyKe+WLHe2MFI+l5R3CiN5Plu61ixbLBXL8+lEueLzz7IXEnUWn0fZa5qu86VEyW+UyqXsa5G+ZkWxb5VqCwB6XVFfYDZP9AMAjslOnOj6huY7fTTfon8yBQAAAAAAKMFgCAAAAAAAMNAskwUAAAAAAH3u1gbq3V0mq582UDcY0kHnz5+PU6daryc+NTUVU1NTXc4IAADK+soPH63c6GYiDBB9JgCgly0tLcXS0lLL2PXrqf0n6UUGQzro4sWLUa1WjzsNAABog4/98NHKtyJipYu5MCj0mQCAXpb6cUa9Xo9ardbljNJuHsMG6jf7aCcOgyEAEBHXTmWFseeX8uKC/0Gi0m+nWvxgIvb1ROzjidjbidgbqWT28eFE7DcTsb+ciL1WGMnzZwtjWTaXqPPRROwbiVhZqfZSsWJ5/unCWJa9kCj5biLW+hfXtzyYiL1TGMnz2US5YulziMjz6UTZ4tc+yxZL5ZO+NsVS559lryTKfSoR+3SiztT7vvgc0te73OtbfK1/ItEWAPS+uax1f2A2T/QFAOgrBkMAAAAAAKDP3YiTcaPLM0O63d5R9M8cFgAAAAAAgBIMhgAAAAAAAAPNMlkAAAAAANDnbsbJ2OnyV/43LZMFAAAAAADQG8wMAeDe8WxWGHrouby43H+QqPPb1xLBtxOx64lYymuJ2BOJ2KlE7HKyxTz/VGEsyx5MlPydRJ3TiTpfStSZOo+y17Ssd0qWK84zy+ZK1vloqfYi3k3Eiq912TzzfDYZz7IXSpVN5VO2XJYtFsZS13S/cyzXXrH0Z6lcnalyRe3V6/Wo1X6tVHsA0G1z2d39gtk80R8A6BM7cSJ2ujxTY6eP5lv0T6YAAAAAAAAlGAwBAAAAAAAGmmWyAAAAAACgz93aQL27y2TZQB0AAAAAAKBH9P1gSKPROO4UAAAAepp+EwDA4NuJE3EjTnb10U8bqPfUMlnb29sxPz8fERELCwstj8mybM+/q9VqbG5u7v67Xq/H/Px8VCqV2N7ejtHR0RgbG9tTpl3HANB75u74/4nbPf8f5sUFX0pU+u1rxbGHTxfHrm4kKv14IvZKIvZuIpbIJd5KxB5LxCKy7EuJ6NuJ2BMl60wprjMdu1yyvdT5pZxKxL5RGMnz2cJYli2WqrO81DmUk2Uvdajs2dL1lpF+neYSJVPX9Hqpcln2QtvrzPPpRDl6gX4TwMEU9Q1m80S/AICB1TODIRsbG7G8vBxra2sxMTHR8piVlZWYmJiI4eHh3edGRkZ2/7vRaEStVovNzc2oVqsRETE8PBxXrlzZrbNdxwAAAHSbfhMAAEV24mTsdPkr/27vUXIUPTMYMjIyEiMjI3f9gul2q6ursb6+XhifnJyMkZGR3RvxiIiZmZmYnJzcvRlv1zEAAADdpt8EAADl9M2CXmtra3Hp0qUYHx+PlZWVu+Lb29uxsbERo6Oje54/d+5cRNz6dVS7jgEAAOhF+k0AANBa3wyGrK+vx/b2dqytrcXk5GQ8/PDDsbHxo7XYL126FBERlUplT7nmr5TW19fbdgwAAEAv0m8CALh33YyTP1wqq3uPm5bJar/l5eVYXl6Oer0ey8vLsbKyEqOjo7G1tRWVSiUajUZERAwNDbUs32g02nbMQb333ntx7Vpi09193H///XH//feXLg8AAAdz44ePKHX/+t5777U5H8rqt36TPhMAcFzef//9eP/990uXdw/cf/pmMKSpWq3G8vJyjI6Oxvj4eMzMzMTq6mpsbW1FRMSZM2daltve3m7bMQf1iU984sDHtjI7OxvPP//8keoAGERziXXSn9+6WVzwLyUq/U4i9uTp4tgbiXJJbydij5Wss+yXSfudxNlE7J2SsWJ5Pl0Yy7LFRMnUeZzqQC4vJEqmzr34embZXKlyKalzSElf65TUtU6/JzpxvVPl8nw2mU85qfO/3oH22vM5e/7552Nu7tb776GHOnFd6LZ+6TfpMwHtUNQ/mM3zLmcC9JP5+fnde+BBcTNOdH1D85v9s/hU/w2GNI2NjcXY2FjU6/WIiBgeHo6IiCtXrrQ8vlKptO2Yg/ryl78cH/nIRw58/J38wgkAgG547rnn4rOf/WxERDz00K8mjiwa0PkfIuKl9iZFW/R6v0mfCQA4LrffA5fx1a9+9cg/7KC7+nYwJCJidHR0d/3b5s120S+QKpVK2445qAceeCBOn078mhgAAHrA3qWGfiJxZNEvbH+8zRnRTr3cb9JnAgCOy1GX23zggQfamA3d0NeDIRER586d2/O/d65N2/x3rVZr2zEAAAD9RL8JAGDw7XRomaz60leivvSVlrEb12+0vb1O6Z8FvVpYX1+PycnJiLi1cV+1Wo319fU9xzR/AfX000+37RgAAIB+od8EAMBRVKc+Fp/5w19s+fhLF/vnvq+nBkOKpljX6/Wo1WqxuPijjTzX1tbizJkzMTY2tvvciy++GBsbG3t+nbSwsBALCwsxNDTU1mMAAACOg34TAACt7MTJuNHlR7c3bD+Knlkmq16vx/LyckREvPzyyzE6OhojIyMxNDQUlUolzpw5E/Pz87G+vh7VajVGR0d3j2+qVquxubkZMzMzUalUotFoxMzMTExMTLT9GAA67P+aFYae/+2iNesj4j9I1PnGa4ng2eLQtxPF4oOJ2JOJWCqXd0rW+UQi9kYi9mAiFpHOJ3HdIrUG/OXCSJYtFsbSTpUsV7Qh9H5S1yWVS6pcyrslyxXLsrnCWJ7PlipX/nqm5fmzhbH0eZQtV+78O1EupXwuxZ+zPJ8+dB63voRfOXQ5Dk+/CbjXzWV39xNm80T/AAB+qGcGQ6rVaiwvL991ox5xayr3ndOvU/Wsrq525RgAAIBu0m8CAIByemYwBAAAAAAAKOdmnIydLn/lf7OPlsnqqT1DAAAAAAAA2s3MEAAAAAAA6HM7caLrG5rv9NF8i/7JFAAAAAAAoAQzQwAAAAAAoM/d2jOkuzND+mnPEIMhABybxSwrjM2M5MUFv5KodONrieC1krG3Ug0mYh9PxH4hEftSIvanErHLidiTiVjqgkZEnCqM5PmnC2NZ9kLJfK4nYqlzTJUrPodULulzSEnlkjr3txOxd0u2V06WLba9zjyfLd1mnk8nSp4tmVHx+6Lb559lc4mSqfdv2VyKr2c6lyLfKp8MALQwV9BXmM0T/QQASLBMFgAAAAAAMNDMDAEAAAAAgD63Eyfihg3UC/VPpgAAAAAAACWYGQIAAAAAAH1uJ+6LnS5/5d/t9o7CzBAAAAAAAGCg9c+wDQB9aS7LCmPPfzkvLvjJkg0+8eHi2OVELOnridhvlqzzlUTs44nY7yRi1xOxU+l0SsqyxUQ0lU/KO4nYo4nYN0q2dzkRezARK3tNU+0VX7M8ny3VWuo1Kl/nC4k6ny1V5y2p651S/J4p+x4tf23mEtGzpdpL1Vk2z5Qyddbr9ajVVtqeCwCDr6i/MJsn+goAUILBEAAAAAAA6HM340TsdHkD9Zt9tPiUwZAOOn/+fJw61fpXo1NTUzE1NdXljAAAoJylpaVYWlpqGbt+vexMMO51+kwAQC9zDzxYDIZ00MWLF6NarR53GgAAcGSpL6ZvLZNV63JGDAJ9JgCgl/XbPfDOMcwM2emjmSH9kykAAAAAAEAJZoYAAAAAAECfuxknj2HPkO62dxQGQwA4srksK4w9/6/lxQV/PVHpjdcSwQeLQ5cfSZSrJ2Jl/Xwi9s1ELJXnu4nYE4nYtUQstZbp2UQsIuIbJcsmXqekVJ1HOY8i7yRiqXMom0txe3k+WxjLsrlEna3X27+lOM8sW0yUSym3Nu5+7eX5dKl601K5Fl+31PVOvU7p17D4/Mu2V7ZcSifqBICI1v2G2TzRXwCANrJMFgAAAAAAMNDMDAEAAAAAgD63Eyfihg3UC/VPpgAAAAAAACWYGQIAAAAAAH1uJ07GTpe/8u/2hu1HYWYIAAAAAAAw0MwMAeBA5rKsMPb5P/7nxQWfSFT67WuJYKpgqlwqdjoRO5uIfTARW0zEUufwRiJWLM8/VRjLspcSJU8lYu+UymV/5drM8+nCWJYVX+90ublEudlS5VLnl+fPJsoVK5/nK4lyqfdMufOLeLQDdUZk2QuJaOo9k7o2qc/o9WQ+7VfcXuoc0oqvadnPCwAcVVHfYTbPu5wJAPyIwRAAAAAAAOhzN+Nk15etummZLAAAAAAAgN5gZggAAAAAAPS5nTjR9ZkhO30038JgCAAAAAAA0NKbS78Tby39vZaxnevf73I25RkMAQAAAACAPrcTJ+NGB2aG/EtTfyH+pam/0DL2vXoj/mHtl9veZicYDAFg11yWFcbW8q8Uxt77P/1kcaX/PNHgfaeLYzeuJwq+kYg9mYh9PBH7UiKW8vOJ2OWSdb5dGMmyFxLl3k3EnkjEUtc6IuLBwkieP1sYy7K5RLnZRLlXErkU55q6Nun2Utf0VCJWLHXuaY+WLHetMJLOJXV+qfdFcSx1rfdT/j1T7hzL1plli4k6p0u1l1L2upRXfM2Kz/2bHcgDgH7Qqg8xm+fHkAkApPXPgl4AAAAAAAAlmBkCAAAAAAB97macjJ0uf+V/s8sbth+FmSEAAAAAAMBAMzOkg86fPx+nTrVec3lqaiqmpqa6nBEAAJT1DyPi9wtiP+hmIgwQfSYAoJctLS3F0tJSy9j16/vtv9l9O3Eidro8U2Onj+ZbGAzpoIsXL0a1Wj3uNAAAoA1+5oePVr4ZEb/WxVwYFPpMAEAvS/04o16vR61W63JGHIXBEIB7zFyWFcae/w/z4oJnEpWm/r//xguJ4IcTsbOJ2COJ2DuJ2EYi9lgi9v9MxFJ5tv6l6y1PJmLXErGUdxOxN0rmEhFxuTCSZYuJcsXXJsu+VKq9iEcTseLXPp1n6tc85dpLv/ap9lLnMJcoVyzPZxN1Fn8+83y6VHvpa52WzrX4/MueYzrXcq9h+TxTr2/qs1T2ehefQyrPIrc6ggZDAAZZUT9iNk/0IQCghxgMAQAAAACAPndrA/XuLpNlA3UAAAAAAIAeYWYIAAAAAAD0uZ04ETdsoF6ofzIFAAAAAAAowcwQAAAAAADocztxMna6/JV/t/coOQozQwAAAAAAgIFmZgjAAJrLssLY81/Oiwv+G4lK/9eJ2G99LRF8NhH7UiL2YCKWci0R+1Qi9koi9mgi9k46nbYr217qHPaTei3eTcTK5nq2MJLnny6MZVnxa5jnxa99ls2VbO+FRLni9326velS5VLKlkvXuVgYS53D/vWWPcfi1yL1Pszz2bbnkpJ+7dufS6rOlLJ5AjAYivoSs3miHwEAfcBgCAAAAAAA9LmbcbLry1bdtEwWAAAAAABAbzAzBAAAAAAA+tzNOHEMM0P6Z75F/2QKAAAAAABQgsEQAAAAAABgoFkmC6BPzWVZYewL+deLC/6bJRv8rVSwnoi9m4hVE7HXErFTqWQS/k4i9mDJOsuWu56IlT2/syXLvb1P/LFE7E8lYsWvYZ4/WxjLssVE7JVEe5cT5eYSucx2tVxK+faKr1lZ3T6Hg8TLtFm+XOpzWPz5LXsO3XaU1wmAwdGqTzGb58eQCQDtsHMMG6h3u72jMDMEAAAAAAAYaGaGAAAAAABAn9uJE3Gj6zND+me+Rf9kCgAAAAAAUIKZIQAAAAAA0Odu7RnS3a/8D7NnyMbGRoyPj8fVq1f3PF+v12N+fj4qlUpsb2/H6OhojI2NtTtVgyEAAAAAAEBnTU5O3vVco9GIWq0Wm5ubUa1WIyJieHg4rly5EhMTE21t32BIB50/fz5OnTrVMjY1NRVTU1NdzgjoJ3NZlow//9/mxcF/L1Hw9UTsn6da/FIi9ulE7OuJ2OVE7IlE7LVE7LFErKzridi7idjZROyRROztROzJROyNROzBRGw/qfP/zUSs+PyzbK5kLq3/f/WW1DkWx7JssTCW57P7p3RIqTrLX5eyUq9tSup1KK4zda0jIvJ8OlG23LUpf72Lz6Nsnely6WtTLPVaFCvz3l5aWoqlpaWWsevXy76XuNfpM0F3FPUtZvNEnwIA98BtNDMzE5VKJa5cubLn+cnJyRgZGdkdCGkeOzk5aTCkn1y8eHHPiwgAAP0q9cV0vV6PWq3W5YwYBPpMAEAv67d74Jtx8lDLVrWrzf1sbGzEBz7wgahWq3Hp0qXd57e3t2NjYyMWFhb2HH/u3LmIiFhZWWnrgIgN1AEAAAAAgI5YXl6O6em7VwJoDoxUKpU9zzd/LLO+vt7WPMwMAQAAAACAPnczThx6Zkj+/vcjf//7pdu88d6fJOMzMzN3zfxoajQaERExNDSUjLeLwRAAAAAAALgHXZ1/Ma7OvdCRuuv1enzgAx+4a+ZH09bWVkREnDlzpmV8e3u7rfkYDAEAAAAAgHvQw89diKHP/pXS5d//6n8f3/rEp1vG5ufnY3V1tbDs8PBwRMRdm6o3FQ2ilGUwBOAYzWVZYewL+dfThf9iIvbflcsnbryWCH68OJT6f5Mb1xPBt0vGRhKxlNMl27tcsr13SsZS16xsLOXBfeKdOP+zJcu9kYgVn3+ezybKFcuyxUSdd693epD2UnV2Rup98WhhJM8/XRjLsrlSmaSu2f5OJWLl3vvl3xep8y/OM/3apz6H5T73qTzLnjsAva2ofzGb513OBIDjcCNOxMnDbqB+/6lbj5JuPvBAy+dnZmZidHR0z1JXzf9u/m9zsKNoBojBEAAAAAAAoGdtbGzE4mLrH4QNDw9HtVqNV199NSLu3huk+e9ardbWnE60tTYAAAAAAKDrbsZ9sdPlx82C+Rabm5uR5/mex/T0dAwNDUWe57G5uRlDQ0NRrVZjfX19T9mNjY2IiHj66afben0MhgAAAAAAAF334osvxsbGxp7ZIQsLC7GwsBBDQ0NtbcsyWQAAAAAA0OduxonYOeyeIW1o8yiq1Wpsbm7GzMxMVCqVaDQaMTMzExMTE23K8EcMhgAAAAAAAB3VnPFxp2q1Gqurqx1v32AIQIfNZVlhbCP/3cLYP/sbT6QrfisRu/y14tjDH04U/HoilsjnxhuJcg8mYo8kYm8nYq8lYimnS5a7noidKlnu0UTsnXQ6BfJ8tjCWZXOJkqk8IyLOliybOsdy7eX5s4Wx9DkWy7LWG7odRbrOsu+nYnk+Xapc6ppl2QuJ9sq+1/bLp+x1K1dn6rqlziN9/uXa67Z+yROAYq36GbN5fgyZAEB/MBgCAAAAAAB9bidOxIkuL5O100fbkvdPpgAAAAAAACWYGQIAAAAAAH3u5s2TsXOzyxuod7m9ozAzBAAAAAAAGGgGQwAAAAAAgIFmmSyANpjLssLY382/XBj7yl/82eJKL+3T6LevJYIfLg5dTZX7eCL2pUTskUTsa4WRPH+2MJZlryTqPJ2IvZ2IlfVoInY9EXsiEbtcss4HCyNZ9kKiXOoc3knEDhIvUy51jqcKI1k2lyh3NlFuMVEupTjPVJ15Pp0ol3qdyl3r8udXfK0j3i1ZZ7H067ef4tc3/bekuM2j5VOk3HumbJ15PluqxvR7tMx1+VapPADYX1FfYzbPu5wJAL1uZ+dExI0ub6C+0z/zLfonUwAAAAAAgBLMDOmg8+fPx6lTrX9xOTU1FVNTU13OCAAAyvrKDx+t3OhmIgwQfSYAoJctLS3F0tJSy9j166kVD47Hzo2TETe6+5X/TpdnohyFwZAOunjxYlSr1eNOAwAA2uBjP3y08q2IWOliLgwKfSYAoJelfpxRr9ejVqt1OSOOwjJZAAAAAADAQDMzBAAAAAAA+tzNnZNd30D95k7/LJNlZggAAAAAADDQzAwBOKC5LCuMPf/f5sUFP5yo9H+XiO23D9e330oE303EHkzEWm9gess7idgjiVixLHspEU1dgFR7Zc/hbCJ2OhH7RiKWeh1Sis8hz58tjGXZC4k6U9ezU5u+pepNvU7F5fJ8tjCWZXOlyqWk6ixbruw5pOT5dKly6fZSr1Eql/af3y1lP0/dlvr8lnudyir/fkq9hosFkZ8o1RYAP1LU35jNE30NALjNzs6JyLs+M6R/5lv0T6YAAAAAAAAlmBkCAAAAAAB9bufGybj5g+7ODOn2TJSjMDMEAAAAAAAYaAZDAAAAAACAgWaZLAAAAAAA6HP5zZOR73T5K/+b/bNMlsEQgNvMZVlh7Pn/V15c8D9JVPpYIvbridjVlxLBiIhfSMS+WRx6+OOJNr+UqPNscei+RC43XkrUeT0RO5WIjSRiryVij5TM5e1ELJVnyoOJ2LuFkSx7qVRref5sos4XStV5S3Gu6XN8JxErvqZlzz8lyxYLY3k+2/Y609e7+HOWZXOFsbJ5pt+/xZ+J9DWbLpnLflKf0WKpa1P2mnbi/Dvx+pY99zK51Ov1qNV+7eDJAdzjWvU7ZvNEfwMAODKDIQAAAAAA0O9unIjo9obmN/pnJ47+yRQAAAAAAKAEgyEAAAAAAMBA66llsra3t2N+fj4iIhYWFu6K1+v1mJ+fj0qlEtvb2zE6OhpjY2PHdgwAAEC36TcBANDSzsnOLJP1G0sR/4+/1Tr2J+X2dzwOPTMYsrGxEcvLy7G2thYTExN3xRuNRtRqtdjc3IxqtRoREcPDw3HlypXd47t5DAAAQLfpNwEA0HX/h6lbj1b+ST3iXz/X3XxKyvI8z487idtlWRYTExOxvLy85/nR0dGIiFhfX999bmVlJSYnJ6N5Ct08JqVer9/VKQB6x1yWFcY+/8f/vDD23md+srjS/6JsNq8lYk/uU/Z0IvZCInZqn3qLPFKy3J9KxC6XjKX8fCL2OyXrTHkiEXsjEUu9vtcSsdQvLt5JxIrl+WwynmVzpcqmyqWdLVkudf7F7/s8ny6MZVnxZynPnz1IUoeqM+LdROzBkuXK/Uqn7GubLre4T5up16K4bLpc+3NNtZfSiTq7LcteKoi8FRFz7n+7qN/7TfpM3CuK+h6zvfVVDAAl9NL9TDOXeGUz4s90OZd/Wo/4VG9ch/30xZ4h29vbsbGxsXuz3XTu3K0Rp5WVla4eAwAA0Gv0mwAAoFhfDIZcunQpIiIqlcqe55sjTevr6109BgAAoNfoNwEA3ON2IuJGlx87XTmztuiZPUNSGo1GREQMDQ0Vxrt5zEG99957ce1aaqmTtPvvvz/uv//+0uUBAOAg3n///Xj//fcPcGTRsmsHKUun9WO/SZ8JADguB78Hbu29995rYzZ0Q18MhmxtbUVExJkzZ1rGt7e3u3rMQX3iE5848LGtzM7OxvPPP3+kOgAAYD/z8/MxN1d2nx96RT/2m/SZAIDj4h743tMXgyHDw8MREXHlypWW8Uql0tVjDurLX/5yfOQjHznw8XfyCycAALrhueeei89+9rP7HvfQQ/95QeSPIuJzbc2Jw+vHfpM+EwBwXA56D1zkq1/96pF/2NF2zWWyut1mn+iLwZDmjXTRr4sqlUpXjzmoBx54IE6fPn3g44H2mcuywtgf5i8Vxv7Ht08VV/pYosEnErG3ErEbH08E307EIiI2ErEPJ2Jll6KoJmK/nog9mYg9UjKXxOsUv1Mylzc6UC6VZ9lyDxZG8ny2MJZli4lY+pcwZestex4R7yZymS6ZS9HSPt0vl76eqdeiuM70tS6Olb2eqXPolHSuxdet/PU+e5C0upJLJ8rd6falhsrUWa/Xo1YzGHLc+rHfpM/EIGnVB5nN82PIBICDOOpymw888EAbs6Eb+mIw5Ny5cxFx97qzzX/XarWuHgMAANBr9JsAAO5xzU3Nu91mnzhx3AkcxNDQUFSr1VhfX9/z/MbGrV9FP/300109BgAAoNfoNwEAQLGeGgxJbbL34osvxsbGxp5fHi0sLMTCwkIMDQ11/RgAAIDjoN8EAACH1zPLZNXr9VheXo6IiJdffjlGR0djZGRk9ya6Wq3G5uZmzMzMRKVSiUajETMzMzExMbFbRzePAQAA6Db9JgAACt2IiB8cQ5t9omcGQ6rVaiwvL+/e2Bcds7q6um893ToGAACgm/SbAACgnJ4ZDAE4rLksK4w9v3WzuOBocbn4p4kG/0widvntRDDllUTsU/uUfSIRezcRO52InU3ELidip0qWS7lWss4HS7aXup7XE7EnCyN5/guFsSxbLNle8WtUts48n02Ui8iyFxLRctc7z59NtFd8Hlk2l6iz+DzS5/BIos79PoeHb6/8OaTKTZcql4qlP9fFyp7fUcqWv27lypWvs/i9vd+1aXe5snUWn/u32p4HQK8q6ofM5nmXMwGAO9yMiJ1jaLNP9NSeIQAAAAAAAO1mZggAAAAAAPS7nej+Hh7dnolyBGaGAAAAAAAAA81gCAAAAAAAMNAskwUAAAAAAP3uRnR/maxut3cEBkOAnjaXZYWx57duFhf81eJy8a8mGtz4enHs2x9MFLyciD2YiI0kYvv5zUTsVCI2nYi9kIg9loilzvHdROyNROxsIvbxRCzxGsZXSraXUnytsyx1Pa+XbO+RROydUjVm2dw+RzyZiKVew7JtFl/TPJ8tVWe63GIil2Jl2ytbZ1llc0npRJ77t5l6nYo/T2XPv+x7LZVnnqf+/hZLX+/U367ivwmdeF8ADJqivshsnnc5EwCgHQyGAAAAAABAv7OBepI9QwAAAAAAgIFmMAQAAAAAABholskCAAAAAIB+Z5msJDNDAAAAAACAgWZmCAAAAAAA9DszQ5IMhnTQ+fPn49SpUy1jU1NTMTU11eWMoDfNZVlh7Fv5rxYX/LvF5eKFRIP/m1Q2HywO/VSi2LcfTAR/JxF7MhF7OxHbr+y1RCx1cT6eiF1OxN5NxJ5IxPY7x9byvDjPLHstUfJsos5nE3WmrtnpROx6Itb6/x/290YiVnx+Ee+UbC8i/do/moh9ozCS57OFsSyb2z+lloqvaZYtJnKZbnt7KeXPr/j1TZ1fWvF7NPUapWIp+5172XrLSudT9jOa+tyXU/bz0u5yS0tLEfGfFZTqdo+LQaHPRK9o1SeZzfNjyASAXrK0tPTD++C7Xb/e/nt/OstgSAddvHgxqtXqcacBAABHNjU1Fb/4i98piH4rIla6mQ4DQp8JAOhlqR9n1Ov1qNVqXc5oH2aGJNkzBAAAAAAAGGgGQwAAAAAAgIFmmSwAAAAAAOh3NyLiBx2o9+8tRfxu671T4vv9s3eKwRAAAAAAAKC1n5u69WjlzXrEcz22d0oBgyFAV8xlWWFsLf9KYeyf/EcfLa70pUSDiWKxkYilfPvtRPBUIvZkIvZEIvZWMp2I04nYI4nYxxOx1MV5IxFLnf+1ROydROxsYSTLXkiUS3k3UediotyDidjlwkieT5dsL/WritS1Lnc9I76RiEXk+WwyXiTL5krFUu2lyqXPsfjapOssls4z9fqWk+fPJtpLfSaKz73b1/ooyr5nyiv+HJa9bp04h86c++Hbu7V5pA3Ugd5X1C+ZzfMuZwIAHbAT3d/Q3AbqAAAAAAAAvcFgCAAAAAAAMNAskwUAAAAAAP1uJ25tot7tNvuEmSEAAAAAAMBAMzMEAAAAAAD6nZkhSQZDgLaZy7LC2B/mLxXGPhNfKIz9X7700XLJ/P7XEsF3E7G3E7EnE7E3ErFTidg7idhjidh+bX48Efv1ROx6InY2EUud4zcKI3k+WxjLslcS5T6VKPdSIpfTidjlRCx1fsWybDERfbBkrOz7qfh12E+WzRXGUq9h+j3TifbK1Zm6pnk+najzpUSdqc9SOen3U9lrXVxn+vNZttwLB0vskMq+Z8q+n8rqdntlr0v69S3+TAD0kqK+yWyedzkTAKBXGAwBAAAAAIB+Z2ZIkj1DAAAAAACAgWYwBAAAAAAAGGiWyQIAAAAAgH53I7q/TFa32zsCM0MAAAAAAICBZmYIcChzWVYY28h/tzD2D/7GaGHs5f/mrxQ3+PuJZP6VRCw+nIi9XRy67+PFsRtfStR5KhH7hUQsVef1RCwi4sFELHGO8cQ+9baW558qjGVZufNIl7ucKDeXKPdkIvZGYSTPZ0u2V9bZRCz12hfH8ny6MJY+h1QuERHv7BMvyufZRD4vJMqlXouXEi2m8iz+jKauW0qef7pUuSxbLJVL6jUsn0vx65C239+n1lLvif3Lpt4XZc+jWCc+9+k6y71H0++L4msGMEha9VFm8/wYMgGAY2YD9SQzQwAAAAAAgIFmZkgHnT9/Pk6dav0rv6mpqZiamupyRgAAUNY/jA996EMtI9evl5stBPpMAEAvW1paiqWlpZYx98D9x2BIB128eDGq1epxpwEAAG3wM/GHf/hftozU6/Wo1WrdTYeBoM8EAPSy1I8zevIe2DJZSZbJAgAAAAAABpqZIQAAAAAA0O9uRMQPjqHNPmEwBLjLXJYVxv5h/v8ujP2Dxkhxpf8k0eDrqWxeKw79/pPlysWfKg4l/4CfTsQeTMS+Vi6XaL1+9o9cKxlLKV7vMsteKdneu4nYVxKx1Oubau/tROxsYSTLFhPlHi2M5PmnE3W+lKjzG4lYSvH7IsvmStZZXpa9kIg+0oEWy163Yp24bnk+m4imPmfFuaTqTL9/e2cd26Nd6+LPb54/e4R6uyf9vihW9rqVfT8B9Kqifspsnnc5EwCgHxkMAQAAAACAfrcT3d/Dw54hAAAAAAAAvcFgCAAAAAAAMNAskwUAAAAAAP1uJ7q/obllsgAAAAAAAHqDmSFwj5rLssLYn80/Vhj7QnyouNL/W3Gd8aVEMjeuJYIfT8RS5U4nYqdK1vlaIvZEyTqP4huJ2JMl63ynMJLnz5aqMcsWE9FHE7G3S+WSZak32xuJ2PVSsSybK4zl+WyiXOq6pHI5W7Lcu4lY8et+S/FnJv1aFF+bVJ1ly+X5dMk6U+/D1DUtvm5lzyH9XnshUa5Y6n3YCWU/E/vXW/yZSV/vlNT7qf3XrWyeZXMp317xZwmgG4r6KrN53uVMAKDPmBmSZGYIAAAAAAAw0AyGAAAAAAAAA80yWQAAAAAA0O8sk5VkZggAAAAAADDQzAwBAAAAAIB+dyMiftCBev9g6dajZZvXO9BgZxgMgQE2l2WFsev584WxqfjfFsb+2c88Udzg/ySRzL+QiH37rUTwkUTsWiL28UTs7ZKxsnWWlWpvP6lrk7qmxa9vlr2UKPeNffIp493CSJYtJsr9fGEkz3+hZJ3lpOssvlnI89lEnXMlszlbslxEKtdO1Jk6/5TUtenMNS2ru6992XKpXDrxGu2nM++L6ZLlyuWSkn7tX0qU+3TJOrt7fgBFWvVZZvP8GDIBAAo9NXXr0crb9YjfqHU3n5IskwUAAAAAAAw0M0MAAAAAAKDf7UT3NzS3gToAAAAAAEBvMDMEAAAAAAD63U7c2kS92232CYMhHXT+/Pk4depUy9jU1FRMTRVsOgMAAD1maWkplpaWWsauX7/e5WwYFPpMAEAvcw88WAyGdNDFixejWq0edxoAAHBkqS+m6/V61Gq1LmfEINBnAgB6Wd/dA5sZkmQwBPrcXJYVxp7/cl5c8K8lKv1OIvZYIvZWIvbtRCxZ6elE7HIilhqdT7X3zUQs1VG/loil8nwiEXstEYuIeCQRe6tkLOXdROxsItb6154REXn+6cJYli2WbK/4emfZfte0tTx/tlS5LHshES1+j6bP/clE7I1E7J1ELHU9I9K5zu1T9vBtps4/z6dLtZa+pmU9moh9IxEr/kyUv57F8ny2VHvdzuVWm8WvU+rzlPqM7tdmu8t1QupvJUC/KOq3zOaJPgsAQBvZQB0AAAAAABhoZoYAAAAAAEC/uxERPziGNvuEmSEAAAAAAMBAMzMEAAAAAAD63U50f0PzPtpA3cwQAAAAAABgoJkZAn1gLssKY382/1hh7Pm/m6h0LBH79xOxfykRey8RS3otETudiF1LxB5MxF4p2V49EXs7ESvr+j7xVJunErF3SuQSkefThbEsS13TN0q1l/bBRCz1fkpd00dL5pJS7lqn8yz7Xit+T+T5s8mSWfZSIvqNUm2mr02qXEqq3BOJWPF7NM9nS+ZSLMsW295eqs6yOnHuR5F6n6avaepvV7lyZaWuafo1LP6b0P33aPuvCzB4WvVfZvP8GDIBAPgRgyEAAAAAANDvdqL7G5pbJgsAAAAAAKA3mBkCAAAAAAD9zsyQJDNDAAAAAACAgWZmCAAAAAAA9LsbEfGDY2izTxgMgR4xl2WFsW/lv1oY+0TjrxVX+iuJBv+7ROzBRCwlVWfqr82N64lgKjaSiL2WiD2SiKW8lYidSsRS5/BGuVQiIuLJROxyIpZ6gT9cGMmyuUS5s4lY8bXJssVEuZTU61v2DfyNwkiWvZIol7rWqevyzn4JtZTnzxbGUq9Rnk8nyr2wT6vv7pdWgdR7v+xnJiV1vct91tLv0eI883w2Ua7cezSVS+r1TUnlmf7Ml3v90tdlv/dpuXzKvoadeO3Tn9H0temusp9B4F5T1IeZzfMuZwIAsD/LZAEAAAAAAAPNzBAAAAAAAOh3N6P7G5rf7HJ7R2BmCAAAAAAAMNDMDAEAAAAAgH53I7q/oXkfbaBuZggAAAAAANB2a2trUavVIsuyqNVqsbGxcdcx9Xo9xsfHY2ZmJiYnJ2Ntba0juZgZ0kHnz5+PU6dOtYxNTU3F1NRUlzPiuM1lWWFsK18pjP3GP7pQXOlfTzT4QCL2Lydi/ywR2/hacey+DxfHblxLVPqpROyVROyNROyJROx6InY5EXsnEWv9Wb/l0UQs5fQ+8VSuZwsjef7pwliWvZCoM3WOjyRi7yZixa9Fns8WxrJsrgN1LibKFb9Hy+ZSVvo1Kiv1GkV05nVqv9R7OyWVZ55PJ8oVv2fSdRZfs5RULmWVfY3K5rJfe92+NmVf+25LvdfKfj7LXOulpaX40Ic+1DqL6+3/e8e9QZ+pPxT1Y2bzvMuZAEB3LS0txdLSUsuYe+D9LS4uxvr6ekxOTsbW1lYsLi7G6OhorK+vx8jISERENBqNqNVqsbm5GdVqNSIihoeH48qVKzExMdHWfAyGdNDFixd3X0AAAOhnqS+m6/V61Gq1LmfEINBnAgB6Wd/dA+9E95etSmzY/vrrr8f6+vruv5955pmo1WqxsLCwOxgyOTkZIyMje+4JmzNE2j0YYpksAAAAAACgbTY2NmJhYWHPc9VqNarVajQajYiI2N7ejo2NjRgdHd1z3Llz5yIiYmWleCWdMgyGAAAAAABAv7sRET/o8qNgJsrIyEhUKpWWsebzly5d2vPvpuYskdtnlbSDZbIAAAAAAOBedPP9W4+ydt471OGNRiMmJyd3/zsiYmhoqPDYdjIYAgAAAAAA/e5mJPfwaOnN+Yi35jqRzV3W1taiUqns7gWytbUVERFnzpxpefz29nZb2zcYAm02l2WFsaG8eNOf3/hrF4orfSzR4M8nYr+RiF2+lgieTsQSydz4eqLcG4nY24nYqZKxsp4o2d5XErFvJGJPJmKp1ygi4uw+8f9/e3cQI8l1Hgb4b0eQtAJJDVc0BQcOJfTSFwcUnJ6lEMQBCMPTBhIFCGLOrE+GfYhnQKzgBDrMQKfhBAEmvYcglwHds0CyEJIDMUPnFMtAzyFKICQxZjq0idgHeTvmISLICLttkshyaa4rh2W3OLtTb2Zruqu6er8PaJDbf71Xr96rV/vevq6qkzUaNwqli3gyEUu1YSrdndxIo3HttALlyG+nRiP1l3vRdPmybDORZ7Hjy7JXJp5nqh3uyz/XUnWTOv6yFS1no/FaIt16It2NM5VrUoqfT8XaaFrtXrSvFZVu+6LnzOTrJn2u5e+vLv0TmC0nzWc2s6yCkgAApfjadyP+1neKp//wzYj/+dKZNt3e3o69vb3xny9duhQREbdu3Tpx+7zHbBVlMQQAAAAAAB5HP/OF+5+i/sYTZ9psY2Mjrl+/fmyBY/T/eXeAWAwBAAAAAACOuxe5LzSf6j5Psbu7G+12e/xi9JHLly9HxMPvBhn9eXFxcTJl/NTPTDQ3AAAAAACAuP+ekIiIpaWlY9/3+/1YWFiIVqsVvV7vWOzg4CAiIq5cuTLRsrgzBAAAAAAA6u6TKP/OkMT+Dg4OYnt7O9bW1mJ3d3f8/dHRUSwuLkar1Yrr16/H4uJiDAaD8WOxOp1OdDqdWFhYmGhRLYYAAAAAAAAT0+/3o91uR0TE2traQ/Hbt29HRESr1Yqjo6PY2NiIZrMZg8EgNjY2YnV1deJlshgCj2ir0UjG/2b2m7mxTvxufsL/nMj0HyVi28niJDyViL2biPUL7u/5ROyFROytgnn+x0Tsn0xhf88mYhcSsfcTsbcTsYiIryVid05Jm+eDRCx1/Kl6S5UzVW+psqTq9MncSJat58YajdcKprtWKJaS3t9WImWqXlJS7RCRbov89k0df+oYU1LHn2WbhWIpWfbKxMuSkj5nivXrVFnS51Mx6b6UrpdpHH9RxeutaD/MN41zraj8svy41HIA55c3r9nMspJLAgDMo1arFdkZxxWtViv29vamXCKLIQAAAAAAUH+fRMRfVbDPmpibF6g/+MZ5AAAAjjNvAgDgcVXbO0MaD9zSO3q22Ei/34/t7e1oNpsxHA6j3W7H8vLysTST2gYAAGAWmTcBADxG/joi7lWwz5qo5WLI7u5urK6uxqVLl8bfLS0tjf9/MBjE4uJiHB0dRavVioiIS5cuxa1bt8YvXpnUNgAAALPIvAkAAH6qloshe3t70ev1cuNra2uxtLQ0HohHRGxsbMTa2tp4MD6pbQAAAGaReRMAwGPmXpT/Do+y70Q5h9q9M2R/fz8ODw9jZWUldnd3H4oPh8M4ODiIdrt97PvLly9HxP1fR01qGwAAgFlk3gQAAMfV7s6QXq8Xw+Ew9vf3Y39/PzY2NmJvb298u/fh4WFERDSbzWPpRr9S6vV649h5t/Erp/m19cCzlT9rIUu3+/ci/9nIN7/3t4sV6EaxZBE/SsTu5Ic+94382CeJdPF+IvZUIvZBwTwPErHnE7E/T8TeLViWRJ0l83wrEbuQiEVEvHdKPM+TuZEsWy+Y56/nRhqNa4XKko6l5NdLo7GVG8uyzULpIr6WiL2diOVL76+YdJ7PniPn/GNM1+lriTxT14R86XMt/9qVKmdRRc+1VB8sel6k6yUl/xqULmd+2552DNNoi7IVv46mnKePProi52+/34/FRf/oPUvMmxjJm9tsZlnJJQEAqFbtFkO63W50u93o9/vR7XZjd3c32u123Lx5M5rNZgwGg4iIWFhYODH9YDCY2Dan+fDDD+P991P/eJr2hS98Ib7whS8UTg8AAGdx9+7duHv37qd/+ih3u7yx7YcffjiFUnEedZk3mTMBAFU5PgZ+dDM5Bv4kyn9MVtn7O4faLYaMtFqt6Ha70W63Y2VlZfxLp5s3b0ZExMWLF09MNxwOJ7bNaV566aVTt0nZ3NyMV1999Vx5AADAaba3t2Nr6/S7j7785X9VQmmYpFmfN5kzAQBVOesYmPlR28WQkeXl5VheXo5+vx8REZcuXYqIiFu3bp24fbPZnNg2p/nBD34Qv/RLv3Tqdnn8wgkAgDJ897vfje985zsREfHlL2/nbveXf/ndE79/8803z/2P2kzXrM6bzJkAgKp8dgxcxEyOgT+JiL+qYJ81UfvFkIiIdrsdBwf33x0wGmzn/QKp2WxObJvTPPHEE/HUU6n3JAAAQPWOP2roi7nb5Y1tn3jiiSmUikmbxXmTORMAUJXzPm7TGLh+fqbqAkzK5cuXj/33wWfTjv68uLg4sW0AAADqxLwJAIDH1VzcGdLr9WJtbS0i7r+4r9VqRa/Xi/X19fE2o19AXblyZWLbUG9bjUZu7O9lfz839muD30tn/O/z841U0vx3lUbc/pNE8OuJ2J1E7M/zQ598kEiXesHlP0jE+olYSuqXgqnjezcRSx1fKs8LidjbidgLidizidhp3kvEUmX9am6k0bhWsCxPJmKpOi2WZ5a9khtrNFLP+8yv70bjxhnKdJJU28+OLNssnLb4eZGSOn/zpY4j1fZFjz99PqWk+uDk93ee9p20VP+sk6Ln0zTOw6J1WrQss3Q+MVnmTfPvpDnOZpZVUBIAoBJ/HRH3KthnTdTqzpB+vx+Li4tx7dpP/1Fmf38/Ll68GMvLy+Pvrl+/HgcHB8d+ndTpdKLT6cTCwsJEtwEAAJgl5k0AAPCwWt0Z0mw24+LFi7G9vR29Xi9arVa02+3odrvHtmu1WnF0dBQbGxvRbDZjMBjExsZGrK6uTnwbAACAWWLeBADwmLoX5b/QvOw7Uc6hVoshCwsL0ev1zrRtq9WKvb29UrYBAACYFeZNAADwsFothgAAAAAAACdwZ0hSrd4ZAgAAAAAA8KjcGcJc22o0cmP/NPtKbuznv/df8zP98JSd/l4i9s4pafM8/Y382AeJdMmV4BcSsTvp8uT6fiL21UTsrUTs2UTsQiL2XiL2fME8U+VMebfg/n7hlHwPErEncyNZ9uu5sUbjWm6s+HmROsaU/JM7Xc78/WXZK4XyzLLNRLqtgmVZL1SWVNumNBqvJaKp/pKWqpui6VLHnz6OySt6fOk2nLz0+Zt/rqXznPy5fZ59Fm+LYnkW3d80ylJUucfw44nvC8iXN8/ZzLKSSwIAUB8WQwAAAAAAoO7KfkRWVfssyGOyAAAAAACAuebOEAAAAAAAqLt7EZH/1oDp7bMm3BkCAAAAAADMNXeGAAAAAAAAJ/toJ+Luzsmx7E65ZTkHiyHU3lYj/96vX8m+mRv7+X/xP/Iz/b3EDj86pUC3U8F3E7GvFszzTxKx/5OI/UUq04RvJGKp43sqEXuhYJ7vJWL5F+Is+/XcWKPxWiLPJxOxZxOxVDlTsdN8LRF7OzfSaGwl0qWOI/WXW9F0qeO/kBvJsvXcWPr4imk0rhVMWWxAUPz4ip6Hp5Vns2B58tuw7LqZxnmRqpfU8aXOp3Se+Yqeo8XrpVj7nba/oudaKl3ROk2ZxvlUVNF6KZonUK68uc5mlpVcEgCgFqb1yKrPXb3/OXGf/Yj/tzilHU+Wx2QBAAAAAABzzZ0hAAAAAABQd/ciouwbSP+65P2dgztDAAAAAACAuebOkCl6+eWX48KFk5+XfvXq1bh6Nec5awAAMHP+6NPPST4psyDMEXMmAGCW7ezsxM7OyS8Ov3NnBl8c/klE5L9eeTpq9CoziyFT9MYbb0Sr1aq6GAAAMAHf/PRzkh9HxG6JZWFemDMBALMs9eOMfr8fi4v1eHE491kMoRa2GvlLml/PruTGXvovr+dn+oeJHb6TiL2QiEVEfJCI/exXE/v8USLhDxOxpxKx9xOxrydif5GIvVswlipLaiX9GwXTPZ8baTSuJdI9mYil/ELBWH7bZtlvJ/eYPo6Tf2F5P9/1RJ5biXSbhdJNQ3p/+ceerrNiUvVSVNH6zLJXJp7naWmncfwpqTYsWpZpnPdl10uqX6fTzc75e5qyy5ra3zTOi/T1Kf/vu2mUpUhd358IWgyB8zhpzrOZ1einlgAAM85iCAAAAAAA1N298JisBC9QBwAAAAAA5po7QwAAAAAAYB7U6E6NsrkzBAAAAAAAmGsWQwAAAAAAgLnmMVnMjK1G/tt9fiX7Zm7spd99vdgO/2+xZPHWaRt8Pz/0TipdKxH7hUTsTxKxr6d2mPBBwdgvJ2LvJmJ3CqZ7LxFbSsTeT8TeTsQuJGKpdkjVWf6xNxq/n0gXEfFkoX02GluJdKljLCbLNguVJcvWE+leS+wxdV4UPb5UO6XqM1+qXorWWdGynC6/3tJtmH8cxaXa4lqiLPnnU9mmcc6k+kSWvVJof9M4n6ZzTkynTosq2n+nYVr1DZxP3rxnM/NMCwCAaXJnCAAAAAAAMNcshgAAAAAAAHPNYggAAAAAADDXLIYAAAAAAABzzWIIAAAAAAAw1yyGAAAAAAAAc+1zVReAx8tWo5EbW8hWc2Mv/W43P9PDxA7/2w/zY5/75UTC9xOxpxKxs8SLeCERe7dgLFXOJxOxC4lYor7j+YJ5ptri2UTsIBH7IBErJsteyY01GjcS6X47ke7aOUp0JxH7Wm6keHny26LR2Co1XZZtFswzdR6mYsWkypI6hlRZsmw9sb9U+6XOl3S+Kal9Fs0zJX38Res7tb9i6aYhfQ1KnfepPIv1pfPUS9llLVvRuil6DEXbqdj+flwgDcy/k+Y/m1lWQUkAgMfDJxHxVxXssx7cGQIAAAAAAMw1d4YAAAAAAEDtfRLl36nhzhAAAAAAAICZYDEEAAAAAACYax6TBQAAAAAAtecF6ikWQ6bo5ZdfjgsXLpwYu3r1aly9erXkEpVjq9HIjf2d7NdyY//4e938TJ9J7PDDVGmezQ/9bCLZO/8hEfxqaocR8X4i9vYpafO8WzD2QSKWfxxZ9kpurNF4LZFnymn1VsR7BdM9WSiWrpetRJ4nXwdOlypnujzTkGXrubH08Sf6YbIN8+st3RbXEuk2C6WLuFMoz5RUX5rG+ZRqv/NIlbVo3aTlH3+6DVPn4eSl27Ae0vU5HcX7U7H6Lv/8LaZoWSadbmdnJ7797VdzUtVnAsRsmZc5U94caDPLSi4JADBJOzs7sbOzc2Lszp38fytgNlkMmaI33ngjWq1W1cUAAIBzu3r1anz72z/Jif44InbLLA5zwpwJAJhlqR9n9Pv9WFxcLLlEp7kX5f9Q6V7J+yvOO0MAAAAAAIC5ZjEEAAAAAACYax6TBQAAAAAAtecF6ikWQwAAAAAAgBz/NiL+XU7sozILci4WQyhkq9HIjf04+ze5sVf/4J/lZ/rPEzu8/aNE8L1E7JfzQ+/8MJHuG4nYk4lYRMSfJ2IXErF3C+Z5p+D+3sqNJJo30vX9bCKWasOUtxOxFxKxou2Qf3yNxlYiXUp+GxXPMy2db+r4U+dTqn3z88yyVxLp8jUa1wrFsmy90P5S6VL1mYpl2WYill8vRc+LadTL/XxfS+Sbf4zTcJ7jyFO0DYumK6povy67jVJOO7enUdaibcjJ8urz/ssjvUCd+Zc3D9rMspJLAgBwkmndGfKbn35O8lZE/MMp7HPyvDMEAAAAAACYa+4MAQAAAACA2rsX5b/D417J+yvOnSEAAAAAAMBcsxgCAAAAAADMNY/JAgAAAACA2pvWC9RP22c9WAwh11ajkRt79b9n+Qn/dSLTf5mI3X731DI9uh8mYj9KxN5LxJ4/ZZ9vJWLPJmIXciNZtp4bazR+v1BZsmwzkedWIs+UVL0VU7ScqXQpjca1RDS/jSKeTMRS9VKs3U+Trrf8Y5zGeVE0XbosrxXKM6X4eV8sz6Ln6HnOi6Ky7JXc2DSOcRqm0xazc3wRd6ouwJmcp87mvw2BWXPSfGgzS8yDAACYaRZDAAAAAACg9rxAPcU7QwAAAAAAgLlmMQQAAAAAAJhrHpMFAAAAAAC15wXqKe4MAQAAAAAA5po7Qx5zW41GbuzVH2T5Cf9uKtd3E7Hvn1akHHcSsQ8SsScTsQu5kSxbz401GluJPCMink3E3kvs85XEPl8rlGdK+jjyjyFVzuL7y2+LaWg0riWiqXMm1X7558y0nH4unizLNktNl5I6hqLHV3R/qeMru66LKnp851H2MRZVl3KmzMMxnMfjfvxlquJaAlXKmxNtZon5EADATPokyr9Tw50hAAAAAAAAM8GdIVP08ssvx4ULJ//i/erVq3H16tWSSwQAAEX9UfziL/7iiZE7d1J38UI+cyYAYJbt7OzEzs7OibHZHAN7Z0iKxZApeuONN6LValVdDAAAmIBvxp/+6X86MdLv92NxcbHk8jAPzJkAgFmW+nGGMXD9eEwWAAAAAAAw19wZAgAAAAAAtXcvyn9s1b2S91ecxZDHwPPxv3JjN+MP8hO+9Foi1w8SsaLPy/tabiTLXsmNNRr55SyartHYSuS5mRs7j0bjWmKf64l0+WWNOPn5y+fJs2jdpGLpY8g3nTyL1nW+aZ1P0zoXy1T0GIq2RVFl1/U8tC2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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -92,10 +94,10 @@
     "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"p_end_velo\"])\n",
     "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n",
     "\n",
-    "stretch_factor = ak.num(trueP_notelectrons, axis=0) / ak.num(trueP_found, axis=0)\n",
+    "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n",
     "\n",
     "nbins = 100\n",
-    "vmax = 100\n",
+    "vmax = 200\n",
     "\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
     "\n",
@@ -107,23 +109,23 @@
     "    cmap=plt.cm.jet,\n",
     "    cmin=1,\n",
     "    vmax=vmax,\n",
-    "    range=[[0, 30000], [0, 30000]],\n",
+    "    range=[[0, 20000], [0, 20000]],\n",
     ")\n",
     "ax0.set_xlabel(f\"True $P$\")\n",
     "ax0.set_ylabel(f\"EndVelo $P$\")\n",
     "ax0.set_title(f\"found P\")\n",
     "\n",
     "a1 = ax1.hist2d(\n",
-    "    trueP_notelectrons,\n",
-    "    endVeloP_notelectrons,\n",
-    "    # trueP_lost,\n",
-    "    # endVeloP_lost,\n",
+    "    # trueP_notelectrons,\n",
+    "    # endVeloP_notelectrons,\n",
+    "    trueP_lost,\n",
+    "    endVeloP_lost,\n",
     "    density=False,\n",
     "    bins=nbins,\n",
     "    cmap=plt.cm.jet,\n",
     "    cmin=1,\n",
     "    vmax=vmax * stretch_factor,\n",
-    "    range=[[0, 30000], [0, 30000]],\n",
+    "    range=[[0, 20000], [0, 20000]],\n",
     ")\n",
     "ax1.set_xlabel(f\"True $P$\")\n",
     "ax1.set_ylabel(f\"EndVelo $P$\")\n",
@@ -136,12 +138,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -158,8 +160,8 @@
     "    bins=nbins,\n",
     "    cmap=plt.cm.jet,\n",
     "    cmin=1,\n",
-    "    vmax=vmax,\n",
-    "    range=[[0, 30000], [0, 30000]],\n",
+    "    vmax=200,\n",
+    "    range=[[0, 20000], [0, 20000]],\n",
     ")\n",
     "plt.xlabel(f\"True $P$ [MeV]\")\n",
     "plt.ylabel(f\"EndVelo $P$ [MeV]\")\n",
diff --git a/trackinglosses_energy.ipynb b/trackinglosses_energy.ipynb
index 78f3468..b6806f2 100644
--- a/trackinglosses_energy.ipynb
+++ b/trackinglosses_energy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -37,10 +37,12 @@
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
-    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
-    "                   (allcolumns.fromB)]  # B: 9056\n",
-    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
-    "                  (allcolumns.fromB)]  # B: 1466\n",
+    "found = allcolumns[\n",
+    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
+    "]  # B: 9056\n",
+    "lost = allcolumns[\n",
+    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
+    "]  # B: 1466\n",
     "\n",
     "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
     "\n",
@@ -51,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -195,7 +197,7 @@
        "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +236,7 @@
        "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,12 +273,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "photon_cut = 0\n",
-    "photon_cut_ratio = 0.25\n",
+    "photon_cut_ratio = 0.2\n",
     "\n",
     "cut_brem = ak.ArrayBuilder()\n",
     "\n",
@@ -294,10 +296,11 @@
     "    cut_brem.field(\"brem_photons_pe\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
-    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "                or brem[itr, \"brem_photons_pe\",\n",
-    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
+    "        if (\n",
+    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            ph_length -= 1\n",
     "            continue\n",
     "        else:\n",
@@ -310,10 +313,11 @@
     "    cut_brem.field(\"brem_vtx_x\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
-    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "                or brem[itr, \"brem_photons_pe\",\n",
-    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
+    "        if (\n",
+    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
     "            cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n",
@@ -325,10 +329,11 @@
     "    cut_brem.field(\"brem_vtx_z\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
-    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "                or brem[itr, \"brem_photons_pe\",\n",
-    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
+    "        if (\n",
+    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
     "            cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n",
@@ -344,14 +349,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "19715\n",
+      "24758\n",
       "50501\n"
      ]
     },
@@ -362,11 +367,11 @@
        " lost: True,\n",
        " rad_length_frac: 0.129,\n",
        " energy: 1.17e+04,\n",
-       " brem_photons_pe: [],\n",
-       " brem_vtx_x: [],\n",
-       " brem_vtx_z: [],\n",
-       " photon_length: 0}\n",
-       "-----------------------------------\n",
+       " brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03],\n",
+       " brem_vtx_x: [-6.97, -52.9, -55.2],\n",
+       " brem_vtx_z: [112, 859, 895],\n",
+       " photon_length: 3}\n",
+       "-------------------------------------------------\n",
        "type: {\n",
        "    event_id: int64,\n",
        "    lost: bool,\n",
@@ -382,7 +387,7 @@
        "<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -492,7 +497,7 @@
        "<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -508,26 +513,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "Z_found = ak.to_numpy(\n",
-    "    ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
-    ") / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
+    "    ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
+    "           keepdims=False)) / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
     "Z_lost = ak.to_numpy(\n",
-    "    ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
-    ") / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
+    "    ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
+    "           keepdims=False)) / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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+ikM3CHV7iBYVQQgAgAXWHRK7vEKsqxuO0ul07M/t9gS1Wq3Yy54lghAAAAui3W4P9MBYliXbtntL6y87Pj5WKpXqnfl17949Sf1DZd3fD+tRGqU77Far1Ybee9vy5oUgBADAgqvX60qlUr3J0dJFEKlWq3r16lVviKzbe+Q4jjzPU61W602k9jyvN/E5ynBXKpXqrRDLZDLyPE9BEMhxHEkXAWsR9hTiiA0AwEIy6VT37p4+3V6WUqmkQqHQW7ZuWZY+fPigZ8+eKZfL9Yat6vV632qy7r5BlUpFhUJBxWJRruvK8zzl83k9ffq09yzp4ggPy7K0ubnZCzi+72tvb0/lclnValXpdFrValW5XE62bater6vRaCifzyufz8+wlcaz0ul0OvOuRFL5vq9MJqNWqzV0WeKkdr856v/5d49jfwYAAKa5zfc3Q2MAAMBYSzM0FoahKpWKpOGn8fq+r0qlIsuyFIahcrncQnTZAQCA6VmKIOR5nlzXVaPR6M2MvywIgoEusnQ6rXa7PfR6AABghqUYGstms6rX69e+XyqVlM1m+8YJHcfpm10PAADMsxRBaJQwDPuWBHZd3v8AAACYaSmGxkY5Pj6WNHgWSrd3qNls3jg8dnp6qs+fP49dh9XVVa2uro59PwAAJjk/P9f5+fnY95+enka+dumDUHfHzGEn6F5+f5RHjx5NVIcXL15o16QNLwAAmEClUtHLly9n8qylD0InJyeSrj9wLsoW4G/fvtXDhw/HrgO9QQAARPf8+XN9++23Y9///v37yJ0YSx+EugfNXbdd+NUhs2HW1tZ09+7dWOsFAACGm3RKydraWuRrl36ydDfoXNfzEyUIAQCA5bT0PULd1WFX5wJ1f85kMjeWsbW1pTt37gx9b2dnRzs7OxPWEgAA3Mb+/r729/eHvnd2dha5nKUPQqlUSrZtq9ls9k7JlS42YZSk7e3tG8t4/fr1VM4aAwAA4xnVEdE9ayyKpRkaGzXp+dWrV/I8r69XqFqtqlqtXruaDAAALL+l6BHyfV+u60qSDg8PlcvllM1meyHHtm21Wi05jiPLshQEgRzH4XgNAAAMt9LpdDrzrkRSdbvW7t+/P5U5QrvfHPX//LvHY5UDAIBpbpoj9P333/edMXqdpegRmjbmCAEAkCzMEQIAAJgQQQgAABiLIAQAAIzFHKEI2FARAIBkYUPFGWKyNAAAycJkaQAAgAkRhAAAgLEIQgAAwFjMEYqAydIAACQLk6VniMnSAAAkC5OlAQAAJkQQAgAAxiIIAQAAYxGEAACAsQhCAADAWKwai4Dl8wAAJAvL52eI5fMAACQLy+cBAAAmRBACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsls9HwD5CAAAkC/sIzRD7CAEAkCzsIwQAADAhghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFhsqBgBO0sDAJAs7Cw9Q+wsDQBAsrCzNAAAwIQIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFoeuRsDp8wAAJAunz88Qp88DAJAsnD4PAAAwIYIQAAAwFkEIAAAYiyAEAACMRRACAADGIggBAABjEYQAAICxCEIAAMBYBCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMbi9Hksvd3d0T8DAMxFjxAAADAWQQgAABiLoTEYJ8rQGMNnAGAGo3qEwjCU4zhyHGfeVQEAAAlgTI+Q53lyXVeNRkPFYnHe1cEsfTzq//kvHs+hEgCAJDImCGWzWWWzWa2srMy7KjDJP+0OvvbLIa8BAObCmCAEjPTx6MoLj2dfBwDAzBGEgCGYUA0AZiAIRXB6eqrPnz+Pff/q6qpWV1djrBGSYFgQIhwBwOTOz891fn4+9v2np6eRryUIRfDo0aOJ7n/x4oV2+YYEACCSSqWily9fzuRZBKEI3r59q4cPH459P71BAABE9/z5c3377bdj3//+/fvInRgEoQjW1tZ09+7deVcDcfl4NMVyHsdTNgAYbNIpJWtra5GvJQgBUX08uvGSgQNe/3IaFQEAxIUgFMHW1pbu3Lkz9L2dnR3t7OzMuEYAAJhtf39f+/v7Q987OzuLXI5RQSgMw7Hue/36tWzbjrcyAABgbKM6InzfVyaTiVSOMUHI9325ritJOjw8VC6XUzabVSqVmm/FkEwfj2K5b/d3jwev+Yf+H4cuKGRHagCYCWOCkG3bcl23F4aAxBgWegAAM2FMEJoEc4QwTUN7jYZd98vp1gMAFglzhGaIOUKI1cejedcAGO5q7yTDsUiwuOYI/SzOSgEAACwSeoSABTHOQbCch3YDJqUDxiMIAUuEkAMAt0MQioDJ0lhm9BoBWERMlp4hJksjkT4e3XzNXzyeciWmYBGGq2Y5qTiuZzERGkuGDRUBzNe0vqCRHIsQSoEJEYQA01z9cvv4eMhFw16b3O43R0Nem8qjkCQEKiQYQQjAjaIEmHnPNYqyYs4Y9LIBkRGEImCyNEwTaan+1R2x/yKmZw/ZaXtgV+2hX/TDXgOwrJgsPUNMlgYi+Hg05MXHs63DZcs6HLOsfy7glpgsDWAsUc82i+VZu9MpZ/cv4yk3NoQTYGERhIBF8fFo3jWYitiC2cejeMqZ5XJ1lrQDc0cQArBwxg1PAz1UQ1bMDZsYftPzo9wTCZOcgZkjCAFIvo9Ht75leFi6fTmYkSjDiwxBYgoIQsAy+3g07xogqcbtfWI47ydR2jBq+9Cuc0MQioDl84A5BnqS/mEu1ZjI0C0IvjmadTWSi96npcDy+Rli+Twwpo9H867B3AzMIxpzn6NI85EGvrQfD15zQ7nXlo34MRcsFiyfB4CEiDJ5O9I13xxNWhUAt0QQAgDEix4PLBCCEAAskFkOaUV51tAz5ma54eUsJxmPE/AIhYlHEAKAhBh7f6SYhubGelZMZ8zNHYHFWD+bdwUAAADmhR4hAACShuX7M0MQioB9hGCcj0fzrgHmYJYH8mJKDNqYkX2EZoh9hABguoZOzP7l7OuBxcE+QgAwCx+P5l2DZPt4NPBSbIfSMoEZM8BkaQAAYCx6hAAAUzXTYS8mGeOW6BECAADGokcIADBzw3akHqucuOYjwVgEIQDA7H08mkqxQ4fhtDuVZ2E5EIQAAInEvkYzYvi8KoIQAMB4kc5r++Zo2tXAHBCEImBnaQAAkoWdpWeInaUBAHOfmD3OBpNLPOzFztIAAIyBuUe4jCAEAFhqBB+MQhACAMAknOHWhyAEAMCUDN3X6JujWVcDIxCEAACYIcJRsnDWGAAAMBY9QgAAxISJ2YuHHiEAAGAsghAAADAWQ2MAAIyBYbDlQI8QAAAwFkEIAAAYi6GxCDh9HgBglKu7TyfwoFZOn58hTp8HAEzT3E+2X0BxnT7P0BgAADAWPUIAACwAeo2mgx4hAABgLHqEAADAaFcnT0uJnEA9DnqEAACAsegRAgAgYdi1enboEQIAAMYiCAEAAGMRhAAAgLGYIwQAwAIaNo+IvYVujx4hAABgLHqEAABYEuw+fXv0CAEAAGMRhAAAgLEYGgMAALd39diNBT1ygx4hAABgLIIQAAAw1kIOjfm+r0qlIsuyFIahcrmc8vl8pHvDMFSlUpEkVavVaVYTAIC5Yq+hmy1cEAqCQJlMRq1WS7ZtS5LS6bTa7baKxeLIez3Pk+u6ajQaN14LAACW38INjZVKJWWz2V4IkiTHcVQqlW68N5vNql6vT7N6AABggSxUj1AYhvI8b2BIa3NzU5JUq9Xo6QEAYAQ2Xey3UD1Cx8fHkiTLsvpe7/YONZvNmdcJAAAsroXqEQqCQJKUSqVGvh+309NTff78eez7V1dXtbq6GmONAABYXufn5zo/Px/7/tPT08jXLlQQOjk5kSRtbGwMfT8Mw6k899GjRxPd/+LFC+3u7sZTGQAAllylUtHLly9n8qyFCkLpdFqS1G63h75/dcgsLm/fvtXDhw/Hvp/eIAAAonv+/Lm+/fbbse9///595E6MhQpC3aBzXc/PtILQ2tqa7t69O5WyAQBAv0mnlKytrUW+dqGCUHd12NW5QN2fM5nMVJ67tbWlO3fuDH1vZ2dHOzs7U3kuAAAYbn9/X/v7+0PfOzs7i1zOQgWhVCol27bVbDZVLpd7r3ueJ0na3t6eynNfv37dt28RAACYr1EdEb7vR+4cWajl85L06tUreZ7X1ytUrVZVrVZ7q8mCIFA6ne4FpMumNaEaAAAsnoXqEZIu9gxqtVpyHEeWZSkIAjmO07eRYhiGarfbA6HH9325ritJOjw8VC6XUzabvXY5PgAAWG4LF4SkizA06qgM27b16dOnoa+7rtsLQwAAmM70g1kXMgjNGpOlAQBIFiMnS88Lk6UBACZZhPPIjJ0sDQAAEBeCEAAAMBZDYxEwRwgAgGRhjtAMMUcIAIBkYY4QAADAhAhCAADAWAQhAABgLIIQAAAwFpOlI2DVGAAAycKqsRli1RgAAMnCqjEAAIAJEYQAAICxCEIAAMBYBCEAAGAsJktHwKoxAACShVVjM8SqMQAAkoVVYwAAABOiRwgAAEzun3ZvvuaXEa6ZMXqEAACAsQhCAADAWAyNAQCAkXZ/93jwtW+OZl2NqaBHCAAAGIseoQjYRwgAgGRhH6EZYh8hAACShX2EAAAAJkQQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFvsIAQCAW7t67MaiHrlBEIqAnaUBAEgWdpaeIXaWBgAgWdhZGgAAYEIEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYxGEAACAsQhCAADAWAQhAABgLIIQAAAwFkEIAAAYiyAEAACMxenzEWxtbenOnTtD3xt1+i0AAJiO/f197e/vD33v7OwscjkEoQhev34t27bnXQ0AAPAfRnVE+L6vTCYTqRyGxgAAgLEIQgAAwFgEIQAAYCyCEAAAMBaTpQEAwMR2f/d48LVvjmZdjVujRwgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBZBCAAAGIsgBAAAjEUQAgAAxiIIAQAAYyXu0FXf91WpVGRZlsIwVC6XUz6fj+2+MAxVqVQkSdVqNfb6AwCAxZGoIBQEgTKZjFqtlmzbliSl02m1220Vi8WJ7/M8T67rqtFojCwPAACYIVFDY6VSSdlsthdmJMlxHJVKpVjuy2azqtfr8VYaAAAsrMQEoTAM5Xmecrlc3+ubm5uSpFqtFut9AAAAiRkaOz4+liRZltX3ereXp9lsDh3OGve+2zg9PdXnz5/Hvn91dVWrq6sT1QEAAFOcn5/r/Px87PtPT08jX5uYIBQEgSQplUqNfD+u+27j0aNHE93/4sUL7e7uTlwPAABMUKlU9PLly5k8KzFB6OTkRJK0sbEx9P0wDGO97zbevn2rhw8fjn0/vUEAAET3/Plzffvtt2Pf//79+8idGIkJQul0WpLUbreHvn916GvS+25jbW1Nd+/enbgcAABws0mnlKytrUW+NjGTpbuB5boenOsCzbj3AQAAJKZHqLvK6+qcnu7PmUwm1vtuY2trS3fu3Bn63s7OjnZ2diZ+BgAAiG5/f1/7+/tD3zs7O4tcTmKCUCqVkm3bajabKpfLvdc9z5MkbW9vx3rfbbx+/bpvjyIAADBfozoifN+P3BGSmKExSXr16pU8z+vr3alWq6pWq71VYUEQKJ1O94JO1Pu64pg8DQAAlkNieoSki71/Wq2WHMeRZVkKgkCO4/TtAxSGodrtdl+giXKfdJEQXdeVJB0eHiqXyymbzV679B4AACy3RAUh6SLUjDoGw7Ztffr06db3da9xXbcXhqJijhAAAMmydHOEkow5QgAAJMtSzhECAACYJYIQAAAwFkEIAAAYizlCETBZGgCAZGGy9AwxWRoAgGRhsjQAAMCECEIAAMBYBCEAAGAsghAAADAWk6UjYNUYAADJwqqxGWLVGAAAycKqMQAAgAkRhAAAgLEIQgAAwFgEIQAAYCwmS0fAqjEAAJKFVWMzxKoxAACShVVjC+78/FxH/n/Rv//pX+ddlaVGO8/Gv//pX2nnGaCdZ4N2js/u7x73/brq/Pxcu7u7Oj8/n33l/gNBaE7Oz8/19v1/1Z/+9G/zrspSo51n409/+jfaeQZo59mgnWfn/PxcL1++JAgBAADMA0EIAAAYiyAEAACMRRACAADGYvl8BOwjBABAsrCP0AyxjxAAAMnCPkIAAAATIggtoeu6CpNc9jTrPC3/43/+N8qekWnVmXbuRzvPxiK2xyK2c1QEoSVEEJqNd//rv1P2jEyrzrRzP9p5NhaxPRaxnaMiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYCyCEAAAMBYbKo7Q3Znyr/7qr7S6ujr0mqdPn2p7e/vWZZ+enkqS/t+P/0f/+T9d7Frt+3fHrGm/s7Mz+b4fS1mzKnta5Q5r57j8+7+f6//+8L9jLXNRy/7Xf7v4b2Ua7SxNrz1o536084VFbedplh1XuVe/57qf0e/fv9fa2tqtyjo8PNTBwcHQ987PzyVF22F6pdPpdG71ZIP8/d//vb7++ut5VwMAAIzh97//vX71q1+NvIYgNMIPP/yg7777Tvfv37/2rDEAAJAsZ2dn+v777/XVV1/p5z//+chrCUIAAMBYTJYGAADGIggBAABjsWpsCnzfV6VSkWVZCsNQuVxO+Xx+aveZatz2ajQaqlQq8n1ftm2rWq0qm83OoMaLKY6/l57nqVAo6NOnT1Oq5eKLo52DIFCj0ZAkFYtFpVKpKdR08U3y2dFsNpVKpRQEgSzLUrVanUGNF08YhqpUKpIUuY3m9h3YQaxOTk46kjqtVqv3mmVZHdd1p3KfqcZtr2q12slmsx3XdTvlcrkjqSOp02w2p13lhRTX30vLsjqpVCru6i2NSdv55OSkk8/nO9lstnNycjKtai6Fcdu6Xq93bNvuey2bzXbK5fJU6rnIms1mJ5/PdyR1isVipHvm+R1IEIpZNpvtZLPZvtdc1+3clDnHvc9U47ZXPp/v+7nVanUkDZSFC3H8vSyXy51sNksQGmGSdm61Wp1UKhX5C8d0k3xGX23jarXasSwr9joui9sEoXl+BzJHKEZhGMrzPOVyub7XNzc3JUm1Wi3W+0w1bnt5njfQRWvbtmzbVhAE06nsAovj76Xnebp3755s255KHZfBJO0chqGePHkiy7Lkuu5U67kMJmnrdrstz/P6Xjs5OZFlWfFX1DDz/g4kCMXo+PhYkgb+w+h+CTSbzVjvM9W47ZXNZq/90OLDbFAcfy9d11W5XI6/cktkknZ2HEdhGDJPJaJJ2rpUKikIAhUKBUkX81kODw9p+xjM+zuQIBSjbq/CdRMUr+t1GPc+U8XdXpc/3PCTSdvZcRy+JCKYpJ27/1JuNpvKZDJaX19XLpfjM+Mak7R1sVhUsVhUo9FQOp2W4zj68OEDvZ0xmPd3IEEoRicnJ5KkjY2Noe+HYRjrfaaKs70ajYYsy1KxWIyjaktlknb2fV/37t2jpy2Ccdu5ezafbdsqlUpqtVpqtVoKgkDpdJrPjSEm/exwXbc3lO553sBQGcYz7+9AglCM0um0pIux5GGu+1IY9z5TxdlelUpF9Xo9lnotm0nauVKpMCQW0bjt3P1XcqlU6l1zea5Qd+kyfjLpZ0cul1OpVOotoS8UCr3tCjC+eX8Hso9QjLr/Z12XXm+an3Lb+0wVV3s5jqNXr17RvtcYt50dxxkYnun+vvu/tPlPxm3n64YRuntiMTw2aJLPjlKpJEm93uMPHz7owYMHevbsGfu9TWje34H0CMWoO8P96gdQ9+dMJhPrfaaKo71qtZpyuRzj+yOM286e56lUKimdTvd+NRoNhWGodDrNfKwrJv3c6A4rXHXdMIPJJvnsODw87Pu8SKVSqlarCsOwN0yJ8cz7O5AgFKNUKiXbtgdmuHfHkbe3t2O9z1STtle3K/vqbtJ8mPUbt51brZY6F3uU9X6Vy2WlUil1Oh21Wq2p132RTPK5kc1mB+apdP9VzT+gBk3y2bGxsTHQY9H9DGEH78nM/Ttw6jsVGaa7Qd/l3V0ty+pUq9XezycnJx3Lsvp2M45yH34ybjs3m82Obdsd13X7fhWLRXbxHmLcdr6qXC6zoeIIk35uXH6tWq0O7ICMn4zb1tVqtZNKpTqfPn3qe422Hu7Tp0/XbqiYtO9A5gjFzLZttVotOY4jy7IUBIEcx+lblRSGodrtdt+/LqLch5+M086+7/c27OqO91/GOViDxv37jNuJ43OjXq8rlUopDEN63UYYt627vZqFQqE3RBaGod68eTPrP0Li+b7fm7R/eHioXC6nbDbb6zlL2nfgSqfT6Uz9KQAAAAnEHCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAYG7mfUAwQQgAAMxNoVCY6870BCEAADBgb29P6+vrWllZ0crKinK5XO9XOp3uvT4J3/dlWdbAwbWzeHYXZ40BAIAB5XJZJycnqtVqKpfLqlarfe8HQdA7v3FcrusOPftxFs/uokcIAAAMdXx8LElDQ4dlWcpmsxOV73netWVM+9ldHLoKAACG6g4/XRcVwjAcGNaKqtFoqNls9k6qn+WzL6NHCAAADPA8T5IGel4ajUbv95MEkYODg6HDYrN49mUEIQAAMKBer0vqH5oKw1AHBwcTlx2GoYIgkG3bM3/2VQQhAAAwoNsrc3BwoEwmo3Q6rfX1dX355ZcTl314eKinT5/O5dlXsWoMAAD06fbYpFIptVqt3mtPnjyJZZKy67p68+ZNbM8ulUpKp9P68ccf9eWXXyqfz0euC0EIAAD0OTw8lNQ/RyeVSimbzV47nBVVEATa2Ni4do7PbZ9dKBRkWZbK5bIkKZPJ9K6PgqExAADQp9lsShpcuv78+fOJy75u76Bxnh0EgRqNRl95T58+Hdh3aBSCEAAA6NOdo7O9vd33+uVenO410kUgSafTymQyvdfCMFQmk+lb6SVdrPwaNXR1m2f7vi/pYl+hLtu25Xle5GM7CEIAAKAnCAKFYTj06IuuWq3Wd1iq4ziqVqsKw7AXUiqVisIw7As9ozZQHOfZ7969G7huY2NDktRut2/6o0pijhAAALik24NzuZelKwxDOY6jWq2mT58+9V5/+vSp8vl8L8iEYai9vb3eMFeX67ojh9du++wwDHvB56ogCIaWcxVBCAAASLo47NRxHEkXvTeZTEYbGxtqt9u91VySlM/n+3piur0+tm0rCAJVKhXl8/mB3h/f96+dbD3Os9PpdG9y9VVRQpDEERsAACAmvu/r4OBAjUZDrVarLyzVajWFYdhb3RWHRqOhQqHQdwyH53nK5XLXHs1xFT1CAAAgFqlUSnt7e6rX6wNzd0btHTSubu/S5WGwUb1OwzBZGgAAxCIMQ2Wz2YFVYTftHTQuy7KUz+f7VqYdHBzcavk8Q2MAACAWjuPo3r17A8NfjuPcesfn27i8s3Q6nVaxWIx8L0EIAABMLAxDra+vy3XdgSBSKBR6B6kmDUNjAABgYrVaTdLw1VpJDUESQQgAAMTg5ORElmXFcijrLDE0BgAAYhGGYewToqeNIAQAAIzF0BgAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhAAAgLEIQgAAwFgEIQAAYKz/D0YdbMwkOz3+AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -569,15 +574,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  15673 , lost:  4042\n",
-      "0.2578957442735915\n"
+      "found:  19848 , lost:  4910\n",
+      "0.2473800886739218\n"
      ]
     },
     {
@@ -585,6 +590,7 @@
       "text/html": [
        "<pre>[-3.61,\n",
        " -33.8,\n",
+       " -133,\n",
        " 65.2,\n",
        " -26.6,\n",
        " 31.6,\n",
@@ -592,25 +598,24 @@
        " -44.7,\n",
        " -103,\n",
        " -10.2,\n",
-       " -47.1,\n",
        " ...,\n",
-       " -25.5,\n",
-       " -90.3,\n",
-       " 55.2,\n",
-       " 152,\n",
-       " -144,\n",
        " 330,\n",
+       " -11.6,\n",
        " -13.1,\n",
+       " -25.6,\n",
        " -4.27,\n",
+       " -103,\n",
+       " 8.82,\n",
+       " 12.8,\n",
        " -17.8]\n",
        "---------------------\n",
-       "type: 15673 * float64</pre>"
+       "type: 19848 * float64</pre>"
       ],
       "text/plain": [
-       "<Array [-3.61, -33.8, 65.2, ..., -13.1, -4.27, -17.8] type='15673 * float64'>"
+       "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='19848 * float64'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -635,12 +640,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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U22pSbqtpOSuHcHk+e4rl+hZLHaNY7t9iub7Fcn2LVa0c+P+OiP80Y/k/FhjXuzEHScnu3LkTN27cmLptd3c3dnd3S44IAACu5vDwMA4PD6du+/d///eSo2FdqDMBAMtMDnzmT79epvlNRPxvJcZydRpISvb48eNIkmTRYQAAwLXN+2P13//938dHH31UckSsA3UmAGCZrV4ObJL2eQyxBQAAAAAAVI4eJAAAAAAAsJbOJmkv+5yrQQ8SAAAAAACgcjSQAAAAAAAAlWOILQAAAAAAWEsmaZ9HD5I1cHh4uOgQ3klR8TpusVbtOqzacYuyatdh1Y5blFW7Dqt23KKs2nVYteMWRbzFHhcWZZnu6bxiyeM4yxRLXpbpd1qmWPKybL/TMpVTHpbpuuR1HNe32OOs4/XNy7pdX8qjgWQNrNrDu2oV+1U7blFW7Tqs2nGLsmrXYdWOW5RVuw6rdtyirNp1WLXjFkW8xR4XFmWZ7ul1/MOa61vcMfI8Th6W7XdapnLKwzJdl7yO4/oWe5x1vL55Wbfrm6+zSdrLXFZnkva1GmJrPB7H3t5eRETs7+9f2J6maezt7UW9Xo/xeBzNZjO2t7cL2QcAAGAZqTcBAMAra9NAMhgMotvtRq/Xi1ardWH7aDSKRqMRw+EwkiSJiIjNzc04PT2d7J/XPgAAAMtIvQkAoGrMQTLP2gyxtbW1FcfHxzO3t9vt2NramiTnERGdTifa7Xbu+wAAACwj9SYAAPjG2jSQzDMej2MwGESz2Ty3/vbt2xERcXR0lNs+AAAAq0i9CQCAqqlEA8mTJ08iIqJer59bf/Ztpn6/n9s+AAAAq0i9CQBgHZU9QfvZshrWZg6SeUajUUREbGxszNye1z6zPHv2LCJeTVT45ZdfXiLq6b7zne/Ed77znQvHTtP0ysec5izGzz//PN5///1cj11EvI77SqXK7de/Pv9vXsd9i1yP+3Xs//7P/xwRFSm3NTpupZ63NTquclvN465cub3l/6jrKLPcvvrqq/jqq6+ufMx/+qd/ioiI3/3ud9eKjfIsut6UZ50pj2clr8+evJ7bPI6zTLGs9PWd8Tm/FNc35zrGUvxOOR+n8vdvwcdxfYs9ztpd3xzz5ryu7z/8wz+sYQ78rxU55xVlayYislardW7dvXv3sojIhsPh1P3r9Xpu+8zyN3/zN1lEWCwWi8VisVgslVp+/vOfXyGrp2gRy1dvUmeyWCwWi8WyLssy5MC/+c1vsu9+97sLuwbf/e53s9/85jeLvgxvVYkeJJubmxERcXp6OnV7vV7PbZ9Z/uzP/ix+/vOfxx//8R/HH/3RH1069jdN60ECAAB5u24Pkt/97nfxL//yL/Hnf/7nOUZFkRZdb1JnAgAWbZ1y4A8//DB+/etfx7/+62J6c3z/+9+PDz/8cCHnfhdL00CSpml0Op1L7Vuv16Pb7V762GcJ+Hg8nrk9r31m+f73vx9/8Rd/cbmAAQAApljnepM6EwBAvj788MOVaKRYpKVpIEmSpLDJ+m7fvh0RF8e6PXvdaDRy2wcAAKAo6k0AAJCfby06gDJsbGxMrUgMBoOIiLh7925u+wAAAKwi9SYAAKpmrRpIZnXhjoh49OhRDAaDc99i2t/fj/39/djY2Mh1Hyjam9/GA4rnuYPF8OxB/tSbKJPPcdaB+5h14D6G6WpZlmWLDiIPaZpGt9uNo6Oj2NjYiEePHsXW1ta55DtN09jb24t6vR6j0SiazWa0Wq0Lx8ljn1X1+u82Ho+j2WzG9vb2osOqvFqtdu51kiQxHA4nry9Tbsq2GOPxOPb29iLiVaX/TXmVjfLL19vKLcJzt8x6vV7s7e1FmqaRJEns7+/H1tbWuX08e8vnMuUW4dlbNq+X29l8Fp631abe9HbuxevxOX596hjFUhconny9WPLqYsl/KyiDr52cnGQRkQ2Hw8m6er2edbvdBUZFt9vNWq1Wtr+/P1leL6PLlJuyLUa/38+2t7eziMhardaF7XmVjfLL19vKLcs8d8tsf38/29rayrrdbnbv3r0sIrKIyPr9/mQfz97yuUy5ZZlnb9mclUe/38/6/X6WJEkWEdnJyclkH88b68a9eD0+x69PHaNY6gLFk68XS15dLPlvNWkgYWJrayvb2to6t67b7Wba0RbrzTKZtv1t5aZsizUruc6rbJRfMeZVijx3y2t7e/vc6+FwmEXEuWvt2Vs+lym3LPPsLZv9/f1zr8/K7fj4eLLO88a6cS9ej8/x/KhjFEtdoDjy9WLJq4sl/62mtZqDhKsbj8cxGAyi2WyeW3/79u2IiDg6OlpEWJXX6/XiyZMnsbOzM7UMLlNuynYx8iob5Vc+z93yGgwGF4ZBSJIkkiSZjKfr2Vs+lym3CM/eMrp3796512dDMCVJEhGeN9aPe/F6fI4Xz+du8dzH1yNfL5a8unjy32rSQEJERDx58iQiIur1+rn1Zx8A/X6/9Jh4dd3H43H0er1ot9tx8+bNGAwGk+2XKTdluxh5lY3yK5/nbnltbW1duKZnztZ79pbPZcotwrO3Cnq9Xuzv73veWFvuxevxOV48n7vFcx9fj3y9WPLq8sl/q0EDCRERk5bm1ydnnLadcnW73ciyLIbDYbRarcmETWflcZlyU7aLkVfZKL/yee5Wz2g0ip2dncnPEZ69VfB6uUV49pZdp9OZTCB5xvPGunEvXo/P8eL53C2e+7gY8vViyauLIf+tDg0kRETEyclJRETcunVr6vbxeFxiNLwpSZLodrtxfHwcEa8+pCMuV27KdjHyKhvltzieu9XQ6/WiXq9Hq9WKCM/eqniz3F7n2Vs+BwcHMRqNYjwenxuqwfPGunEv5sPneHF87pbHfZwf+Xqx5NXFkP9WiwYSIiJic3MzIiJOT0+nbp/VhY9ybW9vx/b2dqRpGhGXKzdluxh5lY3yWzzP3XLb29ubJPwRnr1V8Wa5TePZWx737t2L4+Pj6Pf7sbGxMRn72vPGunEv5svneP587pbPfXx98vViyauLIf+tFg0kRMQ3D96sFkoP5vJoNpuTD9DLlJuyXYy8ykb5LQfP3XLqdDrx6NGjc9fTs7f8ppXbLJ695bK1tRWtVmvS5d/zxrpxL+bP53i+fO4uhvv46uTrxZJXF0/+Ww0aSIiIiNu3b0fExTHuzl43Go3SY2K2s/K6TLkp28XIq2yU3/Lw3C2Xo6OjaDabk0nsznj2ltuscpvHs7dc/uRP/mRSYfO8sW7ci8XwOZ4fn7uL4z5+d/L1YsmryyP/XX8aSIiIV5MCJUkS/X7/3PrBYBAREXfv3l1EWEzR7/ej3W5HxOXKTdkuRl5lo/yWg+duufR6vYh49W2e16Vp6tlbYvPKbRbP3vIZjUaTMvS8sW7ci/nzOZ4vn7uL4T5+d/L1YsmryyX/rYAMvjYcDrOIyE5OTibr6vV6tr+/v8Coqms4HGZJkpy7/sfHx1mr1bqw39vKTdkW5+nTp1lEXCiXLMuvbJRf/maVm+du+fX7/SxJkqzb7Z5bWq1W1u12syzz7C2jt5WbZ2/5PH36NNve3s6Oj48n605OTrKtra1z+3neWDfuxavxOZ4vdYxiqQsUS75eLHl1ceS/1VXLsiwrtgmGVZKmaezt7UW9Xo/RaBTNZjNardaiw6qk8XgcOzs78eTJk7h9+3YkSRLNZvPCNwQiLlduyjZ/aZpGt9uNo6Oj2NjYiEePHsXW1lZsbGyc2yePslF++ZlXbp675Zam6dyuxk+fPp08f5695XGZcosIz94SajabkzJpNptRr9dje3v7wn6eN9aNe/HdyaHyo45RLHWBYsnXiyWvLp78t5o0kAAAAAAAAJVjDhIAAAAAAKByNJAAAAAAAACVo4EEAAAAAACoHA0kAAAAAABA5WggAQAAAAAAKkcDCQAAAAAAUDkaSAAAAAAAgMrRQAIAAAAAAFSOBhIAAAAAAKByvr3oAAAAgLcbjUaxubkZ9Xo9tre3IyLi/v37sbGxsfC4ut1ujMfj+MUvfhHj8TiePn268LgAAFht8l/KoIEEAABWSKfTiVartegwJur1euzv70dExMbGRhwcHCw4IgAA1on8lyIZYgsAAMjFBx98sOgQAACgNPLf1aeBBAAAAAAAqBwNJAAAUKLNzc1oNpuxs7MT7XY7dnZ2olarRa1WizRN3/l44/E4jo6OotlsxtHRUYxGo2g2m3Hz5s1oNpsxHo8jIuLg4CA2Nzfj5s2b0el0cns/AADMI/9lmZmDBAAASrS1tRXdbnfy+uDgIHq9XrRarUiS5J2Pd3p6GsPhMAaDQUREnJycxP7+fpyenk4qovV6PXZ2dqLf70en04mDg4P4+OOPI0mSa78fAADmkf+yzDSQAABAiXZ2diY/j8fj6HQ6sbGxMZno8V3V6/Vot9txdHR0bsLIiIgkSWIwGMTJyUnU6/WIiLh//370er0YDAaRJMm13w8AAPPIf1lmhtgCAIASbW1tTX7+8Y9/HBER+/v7sbGxce1jv3mMs0rdrVu3Lqw7OTnJ/f0AAPAm+S/LTAMJAAAswGAwiF6vF0mSRKvVWnQ4AABQKPkvy0gDCQAALEC73Y6IiOPj4wVHAgAAxZP/sow0kAAAQMk6nU6MRqO4d+/epMs+AACsK/kvy0oDCQAAlGg0GsXBwcHUiSkPDg4WFBUAABRD/ssy00ACAAAlmjW0QJqm8W//9m9XOubp6enU9ePx+ML2s5+nrbvq+wEAYBb5L8tMAwkAAJRkMBjEYDCIiFcVxHa7He12O5rNZjQajdjc3HznY6ZpOvkm3tHRUfR6vcnPZ+fqdDqRpmmMRqPodDqTWI6Ojq79fgAAmEX+y7KrZVmWLToIAABgvtFoFJubm9HtdqPVai06nKkODg6i0+nE06dPY2NjY9HhAACwwuS/lEEPEgAAWCFn3f6X0VWHSAAAgFnkvxTp24sOAAAAuLy9vb1JRez+/fsL/6baaDSKbrcbEWHIAQAAcif/pUiG2AIAAAA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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -698,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -763,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -797,7 +802,7 @@
        "<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -818,16 +823,16 @@
       "found:  41978\n",
       "lost:  8523\n",
       "50501\n",
-      "VELO energy emission, eff:  0.15952159363180926\n",
-      "RICH1+UT energy emission, eff:  0.10419595651571256\n",
-      "Neither, eff:  0.5675135145838697\n",
-      "total efficiency:  0.8312310647313915\n",
+      "VELO energy emission, eff:  0.19057048375279698\n",
+      "RICH1+UT energy emission, eff:  0.1271657986970555\n",
+      "Neither, eff:  0.513494782281539\n",
+      "total efficiency:  0.8312310647313914\n",
       "efficiency:  0.8312310647313914\n",
       "\n",
       "found in velo/(found + lost in velo)\n",
-      "VELO energy emission, eff:  0.8187823965850188\n",
-      "RICH1+UT energy emission, eff:  0.7830357142857143\n",
-      "eff von e die nicht strahlen:  0.8443815921277473\n"
+      "VELO energy emission, eff:  0.8275150472914875\n",
+      "RICH1+UT energy emission, eff:  0.7901082677165354\n",
+      "eff von e die nicht strahlen:  0.8435090915005041\n"
      ]
     }
    ],
@@ -875,8 +880,7 @@
     "\n",
     "eff_rich = num_rich_found / denom\n",
     "\n",
-    "eff_other = ak.num(electrons_found[electrons_found.quality == 0],\n",
-    "                   axis=0) / denom\n",
+    "eff_other = ak.num(electrons_found[electrons_found.quality == 0], axis=0) / denom\n",
     "\n",
     "print(\"VELO energy emission, eff: \", eff_velo)\n",
     "\n",
@@ -904,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -925,12 +929,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energy_emissions = energy_emissions[energy_emissions.energy >= 5e3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -941,14 +954,40 @@
    ],
    "source": [
     "# energyspektren angucken von velo und rich\n",
-    "diff_found = ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (\n",
-    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
-    "        (~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\n",
-    "diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (\n",
-    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
-    "        (energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\n",
+    "diff_found = (\n",
+    "    ak.to_numpy(\n",
+    "        energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "            \"velo\"\n",
+    "        ]\n",
+    "    )\n",
+    "    - ak.to_numpy(\n",
+    "        energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "            \"rich\"\n",
+    "        ]\n",
+    "    )\n",
+    ") / ak.to_numpy(\n",
+    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "        \"energy\"\n",
+    "    ]\n",
+    ")\n",
+    "diff_lost = (\n",
+    "    ak.to_numpy(\n",
+    "        energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "            \"velo\"\n",
+    "        ]\n",
+    "    )\n",
+    "    - ak.to_numpy(\n",
+    "        energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "            \"rich\"\n",
+    "        ]\n",
+    "    )\n",
+    ") / ak.to_numpy(\n",
+    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
+    "        \"energy\"\n",
+    "    ]\n",
+    ")\n",
     "\n",
-    "xlim = 20000\n",
+    "xlim = 1\n",
     "nbins = 80\n",
     "\n",
     "plt.hist(\n",
@@ -971,9 +1010,10 @@
     "    label=\"found\",\n",
     "    range=[-xlim, xlim],\n",
     ")\n",
-    "plt.xlim(-20000, 20000)\n",
-    "plt.title(\"emitted energy difference\")\n",
-    "plt.xlabel(r\"$E_{VELO} - E_{RICH1+UT}$ [MeV]\")\n",
+    "# plt.xlim(-20000, 20000)\n",
+    "# plt.yscale(\"log\")\n",
+    "plt.title(\"emitted energy difference ratio\")\n",
+    "plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})/E_0$\")\n",
     "plt.ylabel(\"a.u.\")\n",
     "plt.legend()\n",
     "plt.show()"
@@ -999,9 +1039,11 @@
     "# number of brem vtx with E>x*E_0\n",
     "\n",
     "number_velo = ak.to_numpy(\n",
-    "    energy_emissions[energy_emissions.quality == 1][\"velo_length\"])\n",
+    "    energy_emissions[energy_emissions.quality == 1][\"velo_length\"]\n",
+    ")\n",
     "number_rich = ak.to_numpy(\n",
-    "    energy_emissions[energy_emissions.quality == 1][\"rich_length\"])\n",
+    "    energy_emissions[energy_emissions.quality == 1][\"rich_length\"]\n",
+    ")\n",
     "\n",
     "plt.hist(\n",
     "    number_velo,\n",
diff --git a/trackinglosses_p_endVelo_endUT.ipynb b/trackinglosses_p_endVelo_endUT.ipynb
new file mode 100644
index 0000000..2e4a18e
--- /dev/null
+++ b/trackinglosses_p_endVelo_endUT.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\t\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import awkward as ak\n",
+    "from scipy.optimize import curve_fit\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
+    "file = uproot.open(\n",
+    "    \"tracking_losses_ntuple_B_rad_length_beginVelo2endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    ")\n",
+    "\n",
+    "# selektiere nur elektronen von B->K*ee\n",
+    "allcolumns = file.arrays()\n",
+    "\n",
+    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = ak.to_numpy(electrons[\"p\"])\n",
+    "p_velo = ak.to_numpy(electrons[\"p_end_velo\"])\n",
+    "p_ut = ak.to_numpy(electrons[\"p_end_ut\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlim = 20000\n",
+    "ax = plt.hist2d(p, p_velo, bins=80, range=[[0, xlim], [0, xlim]], cmin=1, vmax=250)\n",
+    "plt.colorbar(ax[3])\n",
+    "plt.xlabel(f\"True$P$ [MeV]\")\n",
+    "plt.ylabel(f\"EndVELO$P$ [MeV]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlim = 20000\n",
+    "ax = plt.hist2d(p,\n",
+    "                p_ut,\n",
+    "                bins=80,\n",
+    "                range=[[0, xlim], [0, xlim]],\n",
+    "                cmin=1,\n",
+    "                vmax=250)\n",
+    "plt.colorbar(ax[3])\n",
+    "plt.xlabel(f\"True$P$ [MeV]\")\n",
+    "plt.ylabel(f\"EndUT$P$ [MeV]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dvelo = p - p_velo\n",
+    "dut = p - p_ut - dvelo\n",
+    "xlim = 1\n",
+    "nbins = 80\n",
+    "\n",
+    "plt.hist(\n",
+    "    dvelo / p,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"darkorange\",\n",
+    "    label=\"VELO\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "plt.hist(\n",
+    "    dut / p,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"blue\",\n",
+    "    label=\"UT\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "plt.xlim(0, xlim)\n",
+    "# plt.yscale(\"log\")\n",
+    "plt.title(\"emitted energy difference ratio\")\n",
+    "plt.xlabel(r\"$(E_0 - (E_{VELO} , E_{RICH1+UT}))/E_0$\")\n",
+    "plt.ylabel(\"a.u.\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(\n",
+    "    (dut - dvelo) / p,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"blue\",\n",
+    "    label=\"UT\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "# plt.xlim(0, xlim)\n",
+    "# plt.yscale(\"log\")\n",
+    "plt.title(\"emitted energy difference ratio\")\n",
+    "plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})$ [MeV]\")\n",
+    "plt.ylabel(\"a.u.\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tuner",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/trackinglosses_rad_length_beginVelo2endVelo.ipynb b/trackinglosses_rad_length_beginVelo2endVelo.ipynb
index cbc59af..a323111 100644
--- a/trackinglosses_rad_length_beginVelo2endVelo.ipynb
+++ b/trackinglosses_rad_length_beginVelo2endVelo.ipynb
@@ -121,21 +121,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame({\n",
-    "    \"phi\": phi_a * 90.0 / np.pi,\n",
-    "    \"eta\": eta_a,\n",
-    "    \"rad_length_frac\": rad_length_frac_a,\n",
-    "})\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"phi\": phi_a * 90.0 / np.pi,\n",
+    "        \"eta\": eta_a * 2.0,\n",
+    "        \"rad_length_frac\": rad_length_frac_a,\n",
+    "    }\n",
+    ")\n",
     "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,13 +146,13 @@
     "    columns=\"phi\",\n",
     "    values=\"rad_length_frac\",\n",
     "    margins=False,\n",
-    "    fill_value=0,\n",
+    "    # fill_value=0,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -223,996 +225,321 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1.5</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>3.1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.6</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.355900</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>3.3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.30455</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.526900</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.14755</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.135100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.1247</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.7</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.253200</td>\n",
-       "      <td>0.378900</td>\n",
+       "      <th>3.4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.459800</td>\n",
-       "      <td>0.169100</td>\n",
-       "      <td>0.179750</td>\n",
-       "      <td>0.187200</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.228125</td>\n",
-       "      <td>0.177800</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.124700</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.140200</td>\n",
-       "      <td>0.177500</td>\n",
-       "      <td>0.475900</td>\n",
-       "      <td>0.275300</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.244850</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.1691</td>\n",
+       "      <td>0.1710</td>\n",
+       "      <td>0.26650</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.291200</td>\n",
+       "      <td>0.1778</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.14020</td>\n",
+       "      <td>0.1775</td>\n",
+       "      <td>0.5234</td>\n",
+       "      <td>0.1498</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.14810</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.8</th>\n",
-       "      <td>0.141700</td>\n",
-       "      <td>0.420150</td>\n",
-       "      <td>0.311400</td>\n",
-       "      <td>0.370620</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.162100</td>\n",
-       "      <td>0.186800</td>\n",
-       "      <td>0.159033</td>\n",
-       "      <td>0.121133</td>\n",
-       "      <td>0.222767</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.159050</td>\n",
-       "      <td>0.128229</td>\n",
-       "      <td>0.148540</td>\n",
-       "      <td>0.147600</td>\n",
-       "      <td>0.213700</td>\n",
-       "      <td>0.396150</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.417850</td>\n",
-       "      <td>0.216000</td>\n",
-       "      <td>0.116000</td>\n",
+       "      <th>3.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.45340</td>\n",
+       "      <td>0.35060</td>\n",
+       "      <td>0.447200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1885</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1711</td>\n",
+       "      <td>0.243100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.2161</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.16210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.4284</td>\n",
+       "      <td>0.4008</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.24475</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.9</th>\n",
-       "      <td>0.135300</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.164100</td>\n",
-       "      <td>0.388667</td>\n",
-       "      <td>0.399800</td>\n",
-       "      <td>0.243000</td>\n",
-       "      <td>0.165000</td>\n",
-       "      <td>0.143320</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>...</th>\n",
        "      <td>...</td>\n",
-       "      <td>0.157660</td>\n",
-       "      <td>0.189700</td>\n",
-       "      <td>0.159575</td>\n",
-       "      <td>0.180275</td>\n",
-       "      <td>0.193267</td>\n",
-       "      <td>0.321533</td>\n",
-       "      <td>0.484200</td>\n",
-       "      <td>0.376680</td>\n",
-       "      <td>0.431967</td>\n",
-       "      <td>0.368800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>0.353400</td>\n",
-       "      <td>0.308690</td>\n",
-       "      <td>0.303800</td>\n",
-       "      <td>0.396450</td>\n",
-       "      <td>0.436400</td>\n",
-       "      <td>0.269900</td>\n",
-       "      <td>0.170950</td>\n",
-       "      <td>0.177560</td>\n",
-       "      <td>0.151720</td>\n",
-       "      <td>0.162122</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.152460</td>\n",
-       "      <td>0.158525</td>\n",
-       "      <td>0.185100</td>\n",
-       "      <td>0.173757</td>\n",
-       "      <td>0.251557</td>\n",
-       "      <td>0.274000</td>\n",
-       "      <td>0.420800</td>\n",
-       "      <td>0.393471</td>\n",
-       "      <td>0.451720</td>\n",
-       "      <td>0.210150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.1</th>\n",
-       "      <td>0.158200</td>\n",
-       "      <td>0.232540</td>\n",
-       "      <td>0.174500</td>\n",
-       "      <td>0.259733</td>\n",
-       "      <td>0.258775</td>\n",
-       "      <td>0.205350</td>\n",
-       "      <td>0.186400</td>\n",
-       "      <td>0.167020</td>\n",
-       "      <td>0.168629</td>\n",
-       "      <td>0.163200</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.183743</td>\n",
-       "      <td>0.185325</td>\n",
-       "      <td>0.192740</td>\n",
-       "      <td>0.239333</td>\n",
-       "      <td>0.207709</td>\n",
-       "      <td>0.304762</td>\n",
-       "      <td>0.496100</td>\n",
-       "      <td>0.302591</td>\n",
-       "      <td>0.418425</td>\n",
-       "      <td>0.300133</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.2</th>\n",
-       "      <td>0.220000</td>\n",
-       "      <td>0.225210</td>\n",
-       "      <td>0.355058</td>\n",
-       "      <td>0.344350</td>\n",
-       "      <td>0.291310</td>\n",
-       "      <td>0.198650</td>\n",
-       "      <td>0.197100</td>\n",
-       "      <td>0.194667</td>\n",
-       "      <td>0.158688</td>\n",
-       "      <td>0.167200</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.179700</td>\n",
-       "      <td>0.180930</td>\n",
-       "      <td>0.190475</td>\n",
-       "      <td>0.179700</td>\n",
-       "      <td>0.197150</td>\n",
-       "      <td>0.340950</td>\n",
-       "      <td>0.189017</td>\n",
-       "      <td>0.288957</td>\n",
-       "      <td>0.287525</td>\n",
-       "      <td>0.371600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.3</th>\n",
-       "      <td>0.338300</td>\n",
-       "      <td>0.249837</td>\n",
-       "      <td>0.300908</td>\n",
-       "      <td>0.230829</td>\n",
-       "      <td>0.376617</td>\n",
-       "      <td>0.163367</td>\n",
-       "      <td>0.192200</td>\n",
-       "      <td>0.163618</td>\n",
-       "      <td>0.150042</td>\n",
-       "      <td>0.178250</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.181967</td>\n",
-       "      <td>0.188125</td>\n",
-       "      <td>0.184033</td>\n",
-       "      <td>0.213550</td>\n",
-       "      <td>0.218757</td>\n",
-       "      <td>0.164600</td>\n",
-       "      <td>0.316006</td>\n",
-       "      <td>0.378400</td>\n",
-       "      <td>0.296345</td>\n",
-       "      <td>0.267340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.4</th>\n",
-       "      <td>0.299714</td>\n",
-       "      <td>0.245888</td>\n",
-       "      <td>0.354560</td>\n",
-       "      <td>0.263557</td>\n",
-       "      <td>0.330100</td>\n",
-       "      <td>0.240408</td>\n",
-       "      <td>0.202240</td>\n",
-       "      <td>0.182417</td>\n",
-       "      <td>0.175910</td>\n",
-       "      <td>0.164650</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.182113</td>\n",
-       "      <td>0.200911</td>\n",
-       "      <td>0.197689</td>\n",
-       "      <td>0.209911</td>\n",
-       "      <td>0.300017</td>\n",
-       "      <td>0.327933</td>\n",
-       "      <td>0.281250</td>\n",
-       "      <td>0.398044</td>\n",
-       "      <td>0.257986</td>\n",
-       "      <td>0.277720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.5</th>\n",
-       "      <td>0.220817</td>\n",
-       "      <td>0.310787</td>\n",
-       "      <td>0.407230</td>\n",
-       "      <td>0.271920</td>\n",
-       "      <td>0.355700</td>\n",
-       "      <td>0.243955</td>\n",
-       "      <td>0.222678</td>\n",
-       "      <td>0.227625</td>\n",
-       "      <td>0.181771</td>\n",
-       "      <td>0.177013</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.186971</td>\n",
-       "      <td>0.210333</td>\n",
-       "      <td>0.198900</td>\n",
-       "      <td>0.233993</td>\n",
-       "      <td>0.235725</td>\n",
-       "      <td>0.309443</td>\n",
-       "      <td>0.324073</td>\n",
-       "      <td>0.338838</td>\n",
-       "      <td>0.239044</td>\n",
-       "      <td>0.244100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.6</th>\n",
-       "      <td>0.289325</td>\n",
-       "      <td>0.340494</td>\n",
-       "      <td>0.396958</td>\n",
-       "      <td>0.356700</td>\n",
-       "      <td>0.326283</td>\n",
-       "      <td>0.234962</td>\n",
-       "      <td>0.207600</td>\n",
-       "      <td>0.213810</td>\n",
-       "      <td>0.189312</td>\n",
-       "      <td>0.183936</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.202918</td>\n",
-       "      <td>0.204109</td>\n",
-       "      <td>0.231210</td>\n",
-       "      <td>0.205230</td>\n",
-       "      <td>0.258256</td>\n",
-       "      <td>0.365645</td>\n",
-       "      <td>0.380542</td>\n",
-       "      <td>0.456878</td>\n",
-       "      <td>0.311717</td>\n",
-       "      <td>0.340000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.7</th>\n",
-       "      <td>0.377340</td>\n",
-       "      <td>0.309629</td>\n",
-       "      <td>0.408500</td>\n",
-       "      <td>0.395922</td>\n",
-       "      <td>0.457153</td>\n",
-       "      <td>0.245200</td>\n",
-       "      <td>0.208144</td>\n",
-       "      <td>0.227821</td>\n",
-       "      <td>0.204533</td>\n",
-       "      <td>0.200750</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.207200</td>\n",
-       "      <td>0.206678</td>\n",
-       "      <td>0.231667</td>\n",
-       "      <td>0.214758</td>\n",
-       "      <td>0.258890</td>\n",
-       "      <td>0.398944</td>\n",
-       "      <td>0.473256</td>\n",
-       "      <td>0.495275</td>\n",
-       "      <td>0.437533</td>\n",
-       "      <td>0.433650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.8</th>\n",
-       "      <td>0.406600</td>\n",
-       "      <td>0.422255</td>\n",
-       "      <td>0.336575</td>\n",
-       "      <td>0.463889</td>\n",
-       "      <td>0.405786</td>\n",
-       "      <td>0.275860</td>\n",
-       "      <td>0.226343</td>\n",
-       "      <td>0.227864</td>\n",
-       "      <td>0.228122</td>\n",
-       "      <td>0.200971</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.212292</td>\n",
-       "      <td>0.210667</td>\n",
-       "      <td>0.233809</td>\n",
-       "      <td>0.218453</td>\n",
-       "      <td>0.285085</td>\n",
-       "      <td>0.461650</td>\n",
-       "      <td>0.429129</td>\n",
-       "      <td>0.402710</td>\n",
-       "      <td>0.501223</td>\n",
-       "      <td>0.449325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.9</th>\n",
-       "      <td>0.304183</td>\n",
-       "      <td>0.299586</td>\n",
-       "      <td>0.437578</td>\n",
-       "      <td>0.310589</td>\n",
-       "      <td>0.414592</td>\n",
-       "      <td>0.250408</td>\n",
-       "      <td>0.234600</td>\n",
-       "      <td>0.220500</td>\n",
-       "      <td>0.217856</td>\n",
-       "      <td>0.228570</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.245578</td>\n",
-       "      <td>0.240820</td>\n",
-       "      <td>0.224706</td>\n",
-       "      <td>0.230070</td>\n",
-       "      <td>0.255575</td>\n",
-       "      <td>0.248833</td>\n",
-       "      <td>0.352486</td>\n",
-       "      <td>0.193442</td>\n",
-       "      <td>0.296429</td>\n",
-       "      <td>0.450613</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.0</th>\n",
-       "      <td>0.177300</td>\n",
-       "      <td>0.208607</td>\n",
-       "      <td>0.246791</td>\n",
-       "      <td>0.234960</td>\n",
-       "      <td>0.247550</td>\n",
-       "      <td>0.242510</td>\n",
-       "      <td>0.243250</td>\n",
-       "      <td>0.238464</td>\n",
-       "      <td>0.220591</td>\n",
-       "      <td>0.235342</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.242127</td>\n",
-       "      <td>0.230633</td>\n",
-       "      <td>0.234608</td>\n",
-       "      <td>0.220050</td>\n",
-       "      <td>0.246807</td>\n",
-       "      <td>0.214400</td>\n",
-       "      <td>0.207507</td>\n",
-       "      <td>0.253059</td>\n",
-       "      <td>0.225075</td>\n",
-       "      <td>0.279833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.1</th>\n",
-       "      <td>0.176850</td>\n",
-       "      <td>0.174825</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.216220</td>\n",
-       "      <td>0.238011</td>\n",
-       "      <td>0.223344</td>\n",
-       "      <td>0.236530</td>\n",
-       "      <td>0.227418</td>\n",
-       "      <td>0.238523</td>\n",
-       "      <td>0.241125</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.234573</td>\n",
-       "      <td>0.251121</td>\n",
-       "      <td>0.221583</td>\n",
-       "      <td>0.218742</td>\n",
-       "      <td>0.230860</td>\n",
-       "      <td>0.193880</td>\n",
-       "      <td>0.212775</td>\n",
-       "      <td>0.199833</td>\n",
-       "      <td>0.201575</td>\n",
-       "      <td>0.223025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.2</th>\n",
-       "      <td>0.207217</td>\n",
-       "      <td>0.190643</td>\n",
-       "      <td>0.215775</td>\n",
-       "      <td>0.195622</td>\n",
-       "      <td>0.211843</td>\n",
-       "      <td>0.222592</td>\n",
-       "      <td>0.206457</td>\n",
-       "      <td>0.208845</td>\n",
-       "      <td>0.220572</td>\n",
-       "      <td>0.213885</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.255383</td>\n",
-       "      <td>0.226518</td>\n",
-       "      <td>0.242144</td>\n",
-       "      <td>0.234206</td>\n",
-       "      <td>0.246940</td>\n",
-       "      <td>0.251643</td>\n",
-       "      <td>0.234863</td>\n",
-       "      <td>0.194550</td>\n",
-       "      <td>0.191622</td>\n",
-       "      <td>0.224000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.3</th>\n",
-       "      <td>0.212500</td>\n",
-       "      <td>0.178411</td>\n",
-       "      <td>0.200142</td>\n",
-       "      <td>0.205387</td>\n",
-       "      <td>0.247040</td>\n",
-       "      <td>0.214215</td>\n",
-       "      <td>0.205026</td>\n",
-       "      <td>0.204368</td>\n",
-       "      <td>0.234333</td>\n",
-       "      <td>0.206138</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.242873</td>\n",
-       "      <td>0.233411</td>\n",
-       "      <td>0.231791</td>\n",
-       "      <td>0.228750</td>\n",
-       "      <td>0.225615</td>\n",
-       "      <td>0.243327</td>\n",
-       "      <td>0.239650</td>\n",
-       "      <td>0.197650</td>\n",
-       "      <td>0.220333</td>\n",
-       "      <td>0.227175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.4</th>\n",
-       "      <td>0.203900</td>\n",
-       "      <td>0.229470</td>\n",
-       "      <td>0.212793</td>\n",
-       "      <td>0.225382</td>\n",
-       "      <td>0.197680</td>\n",
-       "      <td>0.207650</td>\n",
-       "      <td>0.190145</td>\n",
-       "      <td>0.184869</td>\n",
-       "      <td>0.207871</td>\n",
-       "      <td>0.206950</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.246590</td>\n",
-       "      <td>0.192967</td>\n",
-       "      <td>0.190444</td>\n",
-       "      <td>0.211069</td>\n",
-       "      <td>0.248655</td>\n",
-       "      <td>0.218693</td>\n",
-       "      <td>0.251355</td>\n",
-       "      <td>0.221371</td>\n",
-       "      <td>0.222414</td>\n",
-       "      <td>0.201875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.5</th>\n",
-       "      <td>0.209767</td>\n",
-       "      <td>0.209209</td>\n",
-       "      <td>0.214618</td>\n",
-       "      <td>0.193182</td>\n",
-       "      <td>0.223942</td>\n",
-       "      <td>0.226656</td>\n",
-       "      <td>0.224200</td>\n",
-       "      <td>0.184867</td>\n",
-       "      <td>0.235280</td>\n",
-       "      <td>0.194992</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.231978</td>\n",
-       "      <td>0.235050</td>\n",
-       "      <td>0.226567</td>\n",
-       "      <td>0.215307</td>\n",
-       "      <td>0.216286</td>\n",
-       "      <td>0.228554</td>\n",
-       "      <td>0.227711</td>\n",
-       "      <td>0.227467</td>\n",
-       "      <td>0.241487</td>\n",
-       "      <td>0.225700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.6</th>\n",
-       "      <td>0.203100</td>\n",
-       "      <td>0.199025</td>\n",
-       "      <td>0.162311</td>\n",
-       "      <td>0.218986</td>\n",
-       "      <td>0.214600</td>\n",
-       "      <td>0.202833</td>\n",
-       "      <td>0.237267</td>\n",
-       "      <td>0.190858</td>\n",
-       "      <td>0.205893</td>\n",
-       "      <td>0.208320</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.216200</td>\n",
-       "      <td>0.196613</td>\n",
-       "      <td>0.239618</td>\n",
-       "      <td>0.216460</td>\n",
-       "      <td>0.215436</td>\n",
-       "      <td>0.214009</td>\n",
-       "      <td>0.223700</td>\n",
-       "      <td>0.205814</td>\n",
-       "      <td>0.204694</td>\n",
-       "      <td>0.224175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.7</th>\n",
-       "      <td>0.254600</td>\n",
-       "      <td>0.220037</td>\n",
-       "      <td>0.224288</td>\n",
-       "      <td>0.228317</td>\n",
-       "      <td>0.203760</td>\n",
-       "      <td>0.202433</td>\n",
-       "      <td>0.130050</td>\n",
-       "      <td>0.174056</td>\n",
-       "      <td>0.218980</td>\n",
-       "      <td>0.186362</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.199267</td>\n",
-       "      <td>0.214478</td>\n",
-       "      <td>0.197508</td>\n",
-       "      <td>0.253433</td>\n",
-       "      <td>0.221809</td>\n",
-       "      <td>0.165333</td>\n",
-       "      <td>0.235983</td>\n",
-       "      <td>0.210500</td>\n",
-       "      <td>0.209729</td>\n",
-       "      <td>0.225800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.8</th>\n",
-       "      <td>0.189386</td>\n",
-       "      <td>0.212550</td>\n",
-       "      <td>0.212787</td>\n",
-       "      <td>0.218413</td>\n",
-       "      <td>0.180314</td>\n",
-       "      <td>0.182820</td>\n",
-       "      <td>0.195475</td>\n",
-       "      <td>0.191514</td>\n",
-       "      <td>0.187533</td>\n",
-       "      <td>0.206857</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.212150</td>\n",
-       "      <td>0.189386</td>\n",
-       "      <td>0.240175</td>\n",
-       "      <td>0.226765</td>\n",
-       "      <td>0.200056</td>\n",
-       "      <td>0.213332</td>\n",
-       "      <td>0.218785</td>\n",
-       "      <td>0.229146</td>\n",
-       "      <td>0.201712</td>\n",
-       "      <td>0.208350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.9</th>\n",
-       "      <td>0.152200</td>\n",
-       "      <td>0.173320</td>\n",
-       "      <td>0.202150</td>\n",
-       "      <td>0.224311</td>\n",
-       "      <td>0.203382</td>\n",
-       "      <td>0.180042</td>\n",
-       "      <td>0.179955</td>\n",
-       "      <td>0.224317</td>\n",
-       "      <td>0.201767</td>\n",
-       "      <td>0.182942</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.217258</td>\n",
-       "      <td>0.201900</td>\n",
-       "      <td>0.195956</td>\n",
-       "      <td>0.234673</td>\n",
-       "      <td>0.187436</td>\n",
-       "      <td>0.227500</td>\n",
-       "      <td>0.218291</td>\n",
-       "      <td>0.201937</td>\n",
-       "      <td>0.212855</td>\n",
-       "      <td>0.172500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>0.174950</td>\n",
-       "      <td>0.188591</td>\n",
-       "      <td>0.202700</td>\n",
-       "      <td>0.200275</td>\n",
-       "      <td>0.226100</td>\n",
-       "      <td>0.202900</td>\n",
-       "      <td>0.208282</td>\n",
-       "      <td>0.218286</td>\n",
-       "      <td>0.244600</td>\n",
-       "      <td>0.171700</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.175250</td>\n",
-       "      <td>0.243971</td>\n",
-       "      <td>0.205017</td>\n",
-       "      <td>0.199178</td>\n",
-       "      <td>0.194200</td>\n",
-       "      <td>0.166775</td>\n",
-       "      <td>0.186650</td>\n",
-       "      <td>0.200456</td>\n",
-       "      <td>0.179150</td>\n",
-       "      <td>0.193475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.1</th>\n",
-       "      <td>0.188150</td>\n",
-       "      <td>0.182111</td>\n",
-       "      <td>0.192187</td>\n",
-       "      <td>0.182120</td>\n",
-       "      <td>0.160708</td>\n",
-       "      <td>0.180787</td>\n",
-       "      <td>0.218877</td>\n",
-       "      <td>0.195705</td>\n",
-       "      <td>0.173257</td>\n",
-       "      <td>0.224460</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.199680</td>\n",
-       "      <td>0.186400</td>\n",
-       "      <td>0.223425</td>\n",
-       "      <td>0.223700</td>\n",
-       "      <td>0.198700</td>\n",
-       "      <td>0.211793</td>\n",
-       "      <td>0.210700</td>\n",
-       "      <td>0.168300</td>\n",
-       "      <td>0.200883</td>\n",
-       "      <td>0.202600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.2</th>\n",
-       "      <td>0.204350</td>\n",
-       "      <td>0.204967</td>\n",
-       "      <td>0.157154</td>\n",
-       "      <td>0.230100</td>\n",
-       "      <td>0.194117</td>\n",
-       "      <td>0.193585</td>\n",
-       "      <td>0.167480</td>\n",
-       "      <td>0.154408</td>\n",
-       "      <td>0.174150</td>\n",
-       "      <td>0.241283</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.134962</td>\n",
-       "      <td>0.201320</td>\n",
-       "      <td>0.212757</td>\n",
-       "      <td>0.143350</td>\n",
-       "      <td>0.162450</td>\n",
-       "      <td>0.185694</td>\n",
-       "      <td>0.196530</td>\n",
-       "      <td>0.181363</td>\n",
-       "      <td>0.124100</td>\n",
-       "      <td>0.154300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.3</th>\n",
-       "      <td>0.111900</td>\n",
-       "      <td>0.163650</td>\n",
-       "      <td>0.242429</td>\n",
-       "      <td>0.170043</td>\n",
-       "      <td>0.233867</td>\n",
-       "      <td>0.242429</td>\n",
-       "      <td>0.106900</td>\n",
-       "      <td>0.234722</td>\n",
-       "      <td>0.199713</td>\n",
-       "      <td>0.170850</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.199612</td>\n",
-       "      <td>0.204267</td>\n",
-       "      <td>0.162320</td>\n",
-       "      <td>0.177400</td>\n",
-       "      <td>0.182486</td>\n",
-       "      <td>0.168967</td>\n",
-       "      <td>0.240900</td>\n",
-       "      <td>0.198540</td>\n",
-       "      <td>0.211260</td>\n",
-       "      <td>0.154700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.4</th>\n",
-       "      <td>0.065600</td>\n",
-       "      <td>0.179157</td>\n",
-       "      <td>0.210950</td>\n",
-       "      <td>0.198383</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.248883</td>\n",
-       "      <td>0.156400</td>\n",
-       "      <td>0.232300</td>\n",
-       "      <td>0.203975</td>\n",
-       "      <td>0.224718</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.256600</td>\n",
-       "      <td>0.106900</td>\n",
-       "      <td>0.213975</td>\n",
-       "      <td>0.193763</td>\n",
-       "      <td>0.180425</td>\n",
-       "      <td>0.098260</td>\n",
-       "      <td>0.176700</td>\n",
-       "      <td>0.130213</td>\n",
-       "      <td>0.205100</td>\n",
-       "      <td>0.233800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.5</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.193960</td>\n",
-       "      <td>0.220067</td>\n",
-       "      <td>0.188533</td>\n",
-       "      <td>0.244800</td>\n",
-       "      <td>0.140017</td>\n",
-       "      <td>0.218725</td>\n",
-       "      <td>0.214050</td>\n",
-       "      <td>0.199425</td>\n",
-       "      <td>0.169800</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.237283</td>\n",
-       "      <td>0.173557</td>\n",
-       "      <td>0.187260</td>\n",
-       "      <td>0.238050</td>\n",
-       "      <td>0.184175</td>\n",
-       "      <td>0.165300</td>\n",
-       "      <td>0.247133</td>\n",
-       "      <td>0.185775</td>\n",
-       "      <td>0.235250</td>\n",
-       "      <td>0.209500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.6</th>\n",
-       "      <td>0.229400</td>\n",
-       "      <td>0.155000</td>\n",
-       "      <td>0.116629</td>\n",
-       "      <td>0.205517</td>\n",
-       "      <td>0.221433</td>\n",
-       "      <td>0.203664</td>\n",
-       "      <td>0.148467</td>\n",
-       "      <td>0.273050</td>\n",
-       "      <td>0.160400</td>\n",
-       "      <td>0.176225</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.178225</td>\n",
-       "      <td>0.225750</td>\n",
-       "      <td>0.185767</td>\n",
-       "      <td>0.120300</td>\n",
-       "      <td>0.137700</td>\n",
-       "      <td>0.137350</td>\n",
-       "      <td>0.183275</td>\n",
-       "      <td>0.197100</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.7</th>\n",
-       "      <td>0.184300</td>\n",
-       "      <td>0.232700</td>\n",
-       "      <td>0.159950</td>\n",
-       "      <td>0.144400</td>\n",
-       "      <td>0.161300</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>0.206125</td>\n",
-       "      <td>0.149975</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.186340</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.059500</td>\n",
-       "      <td>0.165750</td>\n",
-       "      <td>0.237600</td>\n",
-       "      <td>0.190820</td>\n",
-       "      <td>0.058800</td>\n",
-       "      <td>0.225250</td>\n",
-       "      <td>0.261800</td>\n",
-       "      <td>0.103200</td>\n",
-       "      <td>0.075550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4.8</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.107667</td>\n",
-       "      <td>0.096550</td>\n",
+       "      <th>9.6</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.06550</td>\n",
+       "      <td>0.09655</td>\n",
        "      <td>0.181325</td>\n",
-       "      <td>0.161300</td>\n",
-       "      <td>0.281700</td>\n",
-       "      <td>0.250350</td>\n",
-       "      <td>0.105450</td>\n",
-       "      <td>0.133400</td>\n",
-       "      <td>0.189200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.199300</td>\n",
-       "      <td>0.069200</td>\n",
-       "      <td>0.282200</td>\n",
-       "      <td>0.265200</td>\n",
-       "      <td>0.163650</td>\n",
-       "      <td>0.075500</td>\n",
-       "      <td>0.108400</td>\n",
-       "      <td>0.202933</td>\n",
-       "      <td>0.067500</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.9</th>\n",
-       "      <td>0.078100</td>\n",
-       "      <td>0.099600</td>\n",
-       "      <td>0.086867</td>\n",
-       "      <td>0.078340</td>\n",
-       "      <td>0.077900</td>\n",
-       "      <td>0.089733</td>\n",
-       "      <td>0.080800</td>\n",
-       "      <td>0.141067</td>\n",
-       "      <td>0.153975</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.123550</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.135033</td>\n",
-       "      <td>0.195800</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.082100</td>\n",
-       "      <td>0.072100</td>\n",
-       "      <td>0.078550</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5.0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.075250</td>\n",
-       "      <td>0.067500</td>\n",
+       "      <td>0.1296</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.25035</td>\n",
+       "      <td>0.0707</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.2469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0692</td>\n",
+       "      <td>0.2822</td>\n",
+       "      <td>0.26520</td>\n",
+       "      <td>0.1244</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.1084</td>\n",
+       "      <td>0.1665</td>\n",
+       "      <td>0.06750</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.7</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>0.0816</td>\n",
+       "      <td>0.0878</td>\n",
+       "      <td>0.07350</td>\n",
+       "      <td>0.0864</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.1993</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.14480</td>\n",
+       "      <td>0.0652</td>\n",
+       "      <td>0.0755</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0796</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.8</th>\n",
+       "      <td>0.0781</td>\n",
+       "      <td>0.09960</td>\n",
+       "      <td>0.08180</td>\n",
+       "      <td>0.083267</td>\n",
+       "      <td>0.0779</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.08810</td>\n",
+       "      <td>0.1684</td>\n",
+       "      <td>0.140867</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0977</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.13015</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0821</td>\n",
+       "      <td>0.0646</td>\n",
+       "      <td>0.08440</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.9</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.09210</td>\n",
+       "      <td>0.08210</td>\n",
+       "      <td>0.071900</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0936</td>\n",
+       "      <td>0.32600</td>\n",
+       "      <td>0.0748</td>\n",
+       "      <td>0.193300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0727</td>\n",
+       "      <td>0.1494</td>\n",
+       "      <td>0.0353</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.3264</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0844</td>\n",
+       "      <td>0.07660</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.05840</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.070600</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.057800</td>\n",
-       "      <td>0.326000</td>\n",
-       "      <td>0.074800</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.072700</td>\n",
-       "      <td>0.078500</td>\n",
-       "      <td>0.072767</td>\n",
-       "      <td>0.030700</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.084400</td>\n",
-       "      <td>0.070900</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0578</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0785</td>\n",
+       "      <td>0.0915</td>\n",
+       "      <td>0.03070</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.07090</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>36 rows × 181 columns</p>\n",
+       "<p>70 rows × 181 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "phi     -90.0     -89.0     -88.0     -87.0     -86.0     -85.0     -84.0  \\\n",
+       "phi    -90.0    -89.0    -88.0     -87.0   -86.0   -85.0    -84.0   -83.0  \\\n",
        "eta                                                                         \n",
-       "1.5  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-       "1.6  0.000000  0.355900  0.000000  0.526900  0.000000  0.000000  0.000000   \n",
-       "1.7  0.000000  0.253200  0.378900  0.459800  0.169100  0.179750  0.187200   \n",
-       "1.8  0.141700  0.420150  0.311400  0.370620  0.000000  0.162100  0.186800   \n",
-       "1.9  0.135300  0.000000  0.164100  0.388667  0.399800  0.243000  0.165000   \n",
-       "2.0  0.353400  0.308690  0.303800  0.396450  0.436400  0.269900  0.170950   \n",
-       "2.1  0.158200  0.232540  0.174500  0.259733  0.258775  0.205350  0.186400   \n",
-       "2.2  0.220000  0.225210  0.355058  0.344350  0.291310  0.198650  0.197100   \n",
-       "2.3  0.338300  0.249837  0.300908  0.230829  0.376617  0.163367  0.192200   \n",
-       "2.4  0.299714  0.245888  0.354560  0.263557  0.330100  0.240408  0.202240   \n",
-       "2.5  0.220817  0.310787  0.407230  0.271920  0.355700  0.243955  0.222678   \n",
-       "2.6  0.289325  0.340494  0.396958  0.356700  0.326283  0.234962  0.207600   \n",
-       "2.7  0.377340  0.309629  0.408500  0.395922  0.457153  0.245200  0.208144   \n",
-       "2.8  0.406600  0.422255  0.336575  0.463889  0.405786  0.275860  0.226343   \n",
-       "2.9  0.304183  0.299586  0.437578  0.310589  0.414592  0.250408  0.234600   \n",
-       "3.0  0.177300  0.208607  0.246791  0.234960  0.247550  0.242510  0.243250   \n",
-       "3.1  0.176850  0.174825  0.200000  0.216220  0.238011  0.223344  0.236530   \n",
-       "3.2  0.207217  0.190643  0.215775  0.195622  0.211843  0.222592  0.206457   \n",
-       "3.3  0.212500  0.178411  0.200142  0.205387  0.247040  0.214215  0.205026   \n",
-       "3.4  0.203900  0.229470  0.212793  0.225382  0.197680  0.207650  0.190145   \n",
-       "3.5  0.209767  0.209209  0.214618  0.193182  0.223942  0.226656  0.224200   \n",
-       "3.6  0.203100  0.199025  0.162311  0.218986  0.214600  0.202833  0.237267   \n",
-       "3.7  0.254600  0.220037  0.224288  0.228317  0.203760  0.202433  0.130050   \n",
-       "3.8  0.189386  0.212550  0.212787  0.218413  0.180314  0.182820  0.195475   \n",
-       "3.9  0.152200  0.173320  0.202150  0.224311  0.203382  0.180042  0.179955   \n",
-       "4.0  0.174950  0.188591  0.202700  0.200275  0.226100  0.202900  0.208282   \n",
-       "4.1  0.188150  0.182111  0.192187  0.182120  0.160708  0.180787  0.218877   \n",
-       "4.2  0.204350  0.204967  0.157154  0.230100  0.194117  0.193585  0.167480   \n",
-       "4.3  0.111900  0.163650  0.242429  0.170043  0.233867  0.242429  0.106900   \n",
-       "4.4  0.065600  0.179157  0.210950  0.198383  0.000000  0.248883  0.156400   \n",
-       "4.5  0.000000  0.193960  0.220067  0.188533  0.244800  0.140017  0.218725   \n",
-       "4.6  0.229400  0.155000  0.116629  0.205517  0.221433  0.203664  0.148467   \n",
-       "4.7  0.184300  0.232700  0.159950  0.144400  0.161300  0.134360  0.206125   \n",
-       "4.8  0.000000  0.107667  0.096550  0.181325  0.161300  0.281700  0.250350   \n",
-       "4.9  0.078100  0.099600  0.086867  0.078340  0.077900  0.089733  0.080800   \n",
-       "5.0  0.000000  0.075250  0.067500  0.070600  0.000000  0.057800  0.326000   \n",
+       "3.1      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
+       "3.2      NaN      NaN      NaN       NaN     NaN     NaN      NaN     NaN   \n",
+       "3.3      NaN  0.30455      NaN  0.526900     NaN     NaN  0.14755     NaN   \n",
+       "3.4      NaN      NaN      NaN  0.459800  0.1691  0.1710  0.26650     NaN   \n",
+       "3.5      NaN  0.45340  0.35060  0.447200     NaN  0.1885      NaN  0.1711   \n",
+       "...      ...      ...      ...       ...     ...     ...      ...     ...   \n",
+       "9.6      NaN  0.06550  0.09655  0.181325  0.1296     NaN  0.25035  0.0707   \n",
+       "9.7      NaN      NaN      NaN  0.070000  0.0816  0.0878  0.07350  0.0864   \n",
+       "9.8   0.0781  0.09960  0.08180  0.083267  0.0779     NaN  0.08810  0.1684   \n",
+       "9.9      NaN  0.09210  0.08210  0.071900     NaN  0.0936  0.32600  0.0748   \n",
+       "10.0     NaN  0.05840      NaN  0.070600     NaN  0.0578      NaN     NaN   \n",
        "\n",
-       "phi     -83.0     -82.0     -81.0  ...      81.0      82.0      83.0  \\\n",
-       "eta                                ...                                 \n",
-       "1.5  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
-       "1.6  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
-       "1.7  0.000000  0.228125  0.177800  ...  0.124700  0.000000  0.000000   \n",
-       "1.8  0.159033  0.121133  0.222767  ...  0.159050  0.128229  0.148540   \n",
-       "1.9  0.143320  0.200000  0.000000  ...  0.157660  0.189700  0.159575   \n",
-       "2.0  0.177560  0.151720  0.162122  ...  0.152460  0.158525  0.185100   \n",
-       "2.1  0.167020  0.168629  0.163200  ...  0.183743  0.185325  0.192740   \n",
-       "2.2  0.194667  0.158688  0.167200  ...  0.179700  0.180930  0.190475   \n",
-       "2.3  0.163618  0.150042  0.178250  ...  0.181967  0.188125  0.184033   \n",
-       "2.4  0.182417  0.175910  0.164650  ...  0.182113  0.200911  0.197689   \n",
-       "2.5  0.227625  0.181771  0.177013  ...  0.186971  0.210333  0.198900   \n",
-       "2.6  0.213810  0.189312  0.183936  ...  0.202918  0.204109  0.231210   \n",
-       "2.7  0.227821  0.204533  0.200750  ...  0.207200  0.206678  0.231667   \n",
-       "2.8  0.227864  0.228122  0.200971  ...  0.212292  0.210667  0.233809   \n",
-       "2.9  0.220500  0.217856  0.228570  ...  0.245578  0.240820  0.224706   \n",
-       "3.0  0.238464  0.220591  0.235342  ...  0.242127  0.230633  0.234608   \n",
-       "3.1  0.227418  0.238523  0.241125  ...  0.234573  0.251121  0.221583   \n",
-       "3.2  0.208845  0.220572  0.213885  ...  0.255383  0.226518  0.242144   \n",
-       "3.3  0.204368  0.234333  0.206138  ...  0.242873  0.233411  0.231791   \n",
-       "3.4  0.184869  0.207871  0.206950  ...  0.246590  0.192967  0.190444   \n",
-       "3.5  0.184867  0.235280  0.194992  ...  0.231978  0.235050  0.226567   \n",
-       "3.6  0.190858  0.205893  0.208320  ...  0.216200  0.196613  0.239618   \n",
-       "3.7  0.174056  0.218980  0.186362  ...  0.199267  0.214478  0.197508   \n",
-       "3.8  0.191514  0.187533  0.206857  ...  0.212150  0.189386  0.240175   \n",
-       "3.9  0.224317  0.201767  0.182942  ...  0.217258  0.201900  0.195956   \n",
-       "4.0  0.218286  0.244600  0.171700  ...  0.175250  0.243971  0.205017   \n",
-       "4.1  0.195705  0.173257  0.224460  ...  0.199680  0.186400  0.223425   \n",
-       "4.2  0.154408  0.174150  0.241283  ...  0.134962  0.201320  0.212757   \n",
-       "4.3  0.234722  0.199713  0.170850  ...  0.199612  0.204267  0.162320   \n",
-       "4.4  0.232300  0.203975  0.224718  ...  0.256600  0.106900  0.213975   \n",
-       "4.5  0.214050  0.199425  0.169800  ...  0.237283  0.173557  0.187260   \n",
-       "4.6  0.273050  0.160400  0.176225  ...  0.000000  0.178225  0.225750   \n",
-       "4.7  0.149975  0.000000  0.186340  ...  0.000000  0.059500  0.165750   \n",
-       "4.8  0.105450  0.133400  0.189200  ...  0.199300  0.069200  0.282200   \n",
-       "4.9  0.141067  0.153975  0.000000  ...  0.000000  0.123550  0.000000   \n",
-       "5.0  0.074800  0.000000  0.000000  ...  0.072700  0.078500  0.072767   \n",
+       "phi      -82.0   -81.0  ...    81.0    82.0    83.0     84.0    85.0    86.0  \\\n",
+       "eta                     ...                                                    \n",
+       "3.1        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
+       "3.2        NaN     NaN  ...     NaN     NaN     NaN      NaN     NaN     NaN   \n",
+       "3.3   0.135100     NaN  ...  0.1247     NaN     NaN      NaN     NaN     NaN   \n",
+       "3.4   0.291200  0.1778  ...     NaN     NaN     NaN  0.14020  0.1775  0.5234   \n",
+       "3.5   0.243100     NaN  ...  0.2161  0.1313     NaN  0.16210     NaN  0.4284   \n",
+       "...        ...     ...  ...     ...     ...     ...      ...     ...     ...   \n",
+       "9.6        NaN  0.2469  ...     NaN  0.0692  0.2822  0.26520  0.1244     NaN   \n",
+       "9.7        NaN     NaN  ...  0.1993     NaN     NaN  0.14480  0.0652  0.0755   \n",
+       "9.8   0.140867     NaN  ...     NaN  0.0977     NaN  0.13015     NaN     NaN   \n",
+       "9.9   0.193300     NaN  ...  0.0727  0.1494  0.0353      NaN  0.3264     NaN   \n",
+       "10.0       NaN     NaN  ...     NaN  0.0785  0.0915  0.03070     NaN     NaN   \n",
        "\n",
-       "phi      84.0      85.0      86.0      87.0      88.0      89.0      90.0  \n",
-       "eta                                                                        \n",
-       "1.5  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-       "1.6  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-       "1.7  0.140200  0.177500  0.475900  0.275300  0.000000  0.244850  0.000000  \n",
-       "1.8  0.147600  0.213700  0.396150  0.000000  0.417850  0.216000  0.116000  \n",
-       "1.9  0.180275  0.193267  0.321533  0.484200  0.376680  0.431967  0.368800  \n",
-       "2.0  0.173757  0.251557  0.274000  0.420800  0.393471  0.451720  0.210150  \n",
-       "2.1  0.239333  0.207709  0.304762  0.496100  0.302591  0.418425  0.300133  \n",
-       "2.2  0.179700  0.197150  0.340950  0.189017  0.288957  0.287525  0.371600  \n",
-       "2.3  0.213550  0.218757  0.164600  0.316006  0.378400  0.296345  0.267340  \n",
-       "2.4  0.209911  0.300017  0.327933  0.281250  0.398044  0.257986  0.277720  \n",
-       "2.5  0.233993  0.235725  0.309443  0.324073  0.338838  0.239044  0.244100  \n",
-       "2.6  0.205230  0.258256  0.365645  0.380542  0.456878  0.311717  0.340000  \n",
-       "2.7  0.214758  0.258890  0.398944  0.473256  0.495275  0.437533  0.433650  \n",
-       "2.8  0.218453  0.285085  0.461650  0.429129  0.402710  0.501223  0.449325  \n",
-       "2.9  0.230070  0.255575  0.248833  0.352486  0.193442  0.296429  0.450613  \n",
-       "3.0  0.220050  0.246807  0.214400  0.207507  0.253059  0.225075  0.279833  \n",
-       "3.1  0.218742  0.230860  0.193880  0.212775  0.199833  0.201575  0.223025  \n",
-       "3.2  0.234206  0.246940  0.251643  0.234863  0.194550  0.191622  0.224000  \n",
-       "3.3  0.228750  0.225615  0.243327  0.239650  0.197650  0.220333  0.227175  \n",
-       "3.4  0.211069  0.248655  0.218693  0.251355  0.221371  0.222414  0.201875  \n",
-       "3.5  0.215307  0.216286  0.228554  0.227711  0.227467  0.241487  0.225700  \n",
-       "3.6  0.216460  0.215436  0.214009  0.223700  0.205814  0.204694  0.224175  \n",
-       "3.7  0.253433  0.221809  0.165333  0.235983  0.210500  0.209729  0.225800  \n",
-       "3.8  0.226765  0.200056  0.213332  0.218785  0.229146  0.201712  0.208350  \n",
-       "3.9  0.234673  0.187436  0.227500  0.218291  0.201937  0.212855  0.172500  \n",
-       "4.0  0.199178  0.194200  0.166775  0.186650  0.200456  0.179150  0.193475  \n",
-       "4.1  0.223700  0.198700  0.211793  0.210700  0.168300  0.200883  0.202600  \n",
-       "4.2  0.143350  0.162450  0.185694  0.196530  0.181363  0.124100  0.154300  \n",
-       "4.3  0.177400  0.182486  0.168967  0.240900  0.198540  0.211260  0.154700  \n",
-       "4.4  0.193763  0.180425  0.098260  0.176700  0.130213  0.205100  0.233800  \n",
-       "4.5  0.238050  0.184175  0.165300  0.247133  0.185775  0.235250  0.209500  \n",
-       "4.6  0.185767  0.120300  0.137700  0.137350  0.183275  0.197100  0.000000  \n",
-       "4.7  0.237600  0.190820  0.058800  0.225250  0.261800  0.103200  0.075550  \n",
-       "4.8  0.265200  0.163650  0.075500  0.108400  0.202933  0.067500  0.000000  \n",
-       "4.9  0.135033  0.195800  0.000000  0.082100  0.072100  0.078550  0.000000  \n",
-       "5.0  0.030700  0.000000  0.000000  0.000000  0.084400  0.070900  0.000000  \n",
+       "phi     87.0    88.0     89.0   90.0  \n",
+       "eta                                   \n",
+       "3.1      NaN     NaN      NaN    NaN  \n",
+       "3.2      NaN     NaN      NaN    NaN  \n",
+       "3.3      NaN     NaN      NaN    NaN  \n",
+       "3.4   0.1498     NaN  0.14810    NaN  \n",
+       "3.5   0.4008     NaN  0.24475    NaN  \n",
+       "...      ...     ...      ...    ...  \n",
+       "9.6   0.1084  0.1665  0.06750    NaN  \n",
+       "9.7      NaN  0.0796      NaN    NaN  \n",
+       "9.8   0.0821  0.0646  0.08440    NaN  \n",
+       "9.9      NaN  0.0844  0.07660    NaN  \n",
+       "10.0     NaN     NaN  0.07090    NaN  \n",
        "\n",
-       "[36 rows x 181 columns]"
+       "[70 rows x 181 columns]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1223,12 +550,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1240,14 +567,15 @@
    "source": [
     "ax = sns.heatmap(\n",
     "    df_pivoted,\n",
-    "    robust=True,\n",
+    "    robust=False,\n",
     "    square=False,\n",
     "    cmap=colormaps[\"rainbow\"],\n",
     "    xticklabels=False,\n",
     "    yticklabels=False,\n",
     "    vmax=0.8,\n",
     ")\n",
-    "ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
+    "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
+    "ax.set_yticks([10, 30, 50, 70], [2, 3, 4, 5])\n",
     "ax.set_xticks([39, 89, 139], [-100, 0, 100])\n",
     "# ax.set_xticks([79, 179, 279], [-100, 0, 100])\n",
     "ax.set_xlabel(f\"$\\phi$ [deg]\")\n",
diff --git a/trackinglosses_rad_length_endVelo2endUT.ipynb b/trackinglosses_rad_length_endVelo2endUT.ipynb
index 71e53bc..5411dd9 100644
--- a/trackinglosses_rad_length_endVelo2endUT.ipynb
+++ b/trackinglosses_rad_length_endVelo2endUT.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -119,12 +119,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -521,23 +521,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"phi\": phi_a * 90.0 / np.pi,\n",
-    "        \"eta\": eta_a,\n",
-    "        \"rad_length_frac\": rad_length_frac_a,\n",
-    "    }\n",
-    ")\n",
+    "df = pd.DataFrame({\n",
+    "    \"phi\": phi_a * 90.0 / np.pi,\n",
+    "    \"eta\": eta_a * 2.0,\n",
+    "    \"rad_length_frac\": rad_length_frac_a,\n",
+    "})\n",
     "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -625,996 +623,321 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1.5</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.6</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.745900</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.746800</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.7</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.178400</td>\n",
-       "      <td>0.295200</td>\n",
-       "      <td>0.743500</td>\n",
-       "      <td>0.379000</td>\n",
-       "      <td>0.614800</td>\n",
-       "      <td>1.693300</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.930075</td>\n",
-       "      <td>0.111500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.381300</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.380250</td>\n",
-       "      <td>1.114300</td>\n",
-       "      <td>0.730200</td>\n",
-       "      <td>0.420600</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.377950</td>\n",
+       "      <th>3.1</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.8</th>\n",
-       "      <td>0.381100</td>\n",
-       "      <td>0.386050</td>\n",
-       "      <td>0.243450</td>\n",
-       "      <td>0.168900</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.229833</td>\n",
-       "      <td>0.229900</td>\n",
-       "      <td>0.390667</td>\n",
-       "      <td>0.165900</td>\n",
-       "      <td>0.327267</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.223500</td>\n",
-       "      <td>0.236243</td>\n",
-       "      <td>0.883980</td>\n",
-       "      <td>0.270350</td>\n",
-       "      <td>1.366700</td>\n",
-       "      <td>0.160850</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.272100</td>\n",
-       "      <td>0.234500</td>\n",
-       "      <td>0.124500</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.9</th>\n",
-       "      <td>0.153000</td>\n",
+       "      <th>3.2</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.142200</td>\n",
-       "      <td>0.239467</td>\n",
-       "      <td>0.295500</td>\n",
-       "      <td>0.150900</td>\n",
-       "      <td>0.273500</td>\n",
-       "      <td>0.161000</td>\n",
-       "      <td>0.867600</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.220200</td>\n",
-       "      <td>0.171267</td>\n",
-       "      <td>0.163900</td>\n",
-       "      <td>0.151325</td>\n",
-       "      <td>0.154617</td>\n",
-       "      <td>0.251200</td>\n",
-       "      <td>0.139100</td>\n",
-       "      <td>0.191800</td>\n",
-       "      <td>0.224867</td>\n",
-       "      <td>0.144000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>0.151000</td>\n",
-       "      <td>0.137790</td>\n",
-       "      <td>0.139500</td>\n",
-       "      <td>0.141687</td>\n",
-       "      <td>0.162550</td>\n",
-       "      <td>0.160067</td>\n",
-       "      <td>0.161450</td>\n",
-       "      <td>0.149060</td>\n",
-       "      <td>0.169400</td>\n",
-       "      <td>0.176289</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.172440</td>\n",
-       "      <td>0.176475</td>\n",
-       "      <td>0.153450</td>\n",
-       "      <td>0.152857</td>\n",
-       "      <td>0.178971</td>\n",
-       "      <td>0.154686</td>\n",
-       "      <td>0.141500</td>\n",
-       "      <td>0.140043</td>\n",
-       "      <td>0.141740</td>\n",
-       "      <td>0.134250</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2.1</th>\n",
-       "      <td>0.132200</td>\n",
-       "      <td>0.137630</td>\n",
-       "      <td>0.147300</td>\n",
-       "      <td>0.136700</td>\n",
-       "      <td>0.139863</td>\n",
-       "      <td>0.165500</td>\n",
-       "      <td>0.150780</td>\n",
-       "      <td>0.149290</td>\n",
-       "      <td>0.152229</td>\n",
-       "      <td>0.168450</td>\n",
+       "      <th>3.3</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.96215</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.746800</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.7263</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.381400</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.165614</td>\n",
-       "      <td>0.155900</td>\n",
-       "      <td>0.150020</td>\n",
-       "      <td>0.148483</td>\n",
-       "      <td>0.160091</td>\n",
-       "      <td>0.141550</td>\n",
-       "      <td>0.143433</td>\n",
-       "      <td>0.137018</td>\n",
-       "      <td>0.137937</td>\n",
-       "      <td>0.137000</td>\n",
+       "      <td>0.3813</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2.2</th>\n",
-       "      <td>0.138267</td>\n",
-       "      <td>0.136160</td>\n",
-       "      <td>0.137342</td>\n",
-       "      <td>0.136750</td>\n",
-       "      <td>0.138395</td>\n",
-       "      <td>0.161375</td>\n",
-       "      <td>0.155864</td>\n",
-       "      <td>0.150350</td>\n",
-       "      <td>0.148888</td>\n",
-       "      <td>0.153114</td>\n",
+       "      <th>3.4</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.743500</td>\n",
+       "      <td>0.37900</td>\n",
+       "      <td>0.11090</td>\n",
+       "      <td>1.6273</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.380200</td>\n",
+       "      <td>0.1115</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.155750</td>\n",
-       "      <td>0.149120</td>\n",
-       "      <td>0.148875</td>\n",
-       "      <td>0.158500</td>\n",
-       "      <td>0.159117</td>\n",
-       "      <td>0.139737</td>\n",
-       "      <td>0.136467</td>\n",
-       "      <td>0.136900</td>\n",
-       "      <td>0.136238</td>\n",
-       "      <td>0.146150</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.38025</td>\n",
+       "      <td>1.1143</td>\n",
+       "      <td>0.7429</td>\n",
+       "      <td>0.1109</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.378400</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2.3</th>\n",
-       "      <td>0.140120</td>\n",
-       "      <td>0.135150</td>\n",
-       "      <td>0.136392</td>\n",
-       "      <td>0.136000</td>\n",
-       "      <td>0.135283</td>\n",
-       "      <td>0.138733</td>\n",
-       "      <td>0.162330</td>\n",
-       "      <td>0.156182</td>\n",
-       "      <td>0.147925</td>\n",
-       "      <td>0.145092</td>\n",
+       "      <th>3.5</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.38280</td>\n",
+       "      <td>0.33605</td>\n",
+       "      <td>0.110300</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.11870</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.23330</td>\n",
+       "      <td>1.479350</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.159367</td>\n",
-       "      <td>0.148900</td>\n",
-       "      <td>0.155467</td>\n",
-       "      <td>0.160300</td>\n",
-       "      <td>0.145629</td>\n",
-       "      <td>0.145200</td>\n",
-       "      <td>0.139894</td>\n",
-       "      <td>0.141271</td>\n",
-       "      <td>0.138073</td>\n",
-       "      <td>0.141620</td>\n",
+       "      <td>0.1458</td>\n",
+       "      <td>0.39860</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.39800</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.7175</td>\n",
+       "      <td>0.7303</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.243900</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2.4</th>\n",
-       "      <td>0.146343</td>\n",
-       "      <td>0.137150</td>\n",
-       "      <td>0.140820</td>\n",
-       "      <td>0.137143</td>\n",
-       "      <td>0.134425</td>\n",
-       "      <td>0.138600</td>\n",
-       "      <td>0.153840</td>\n",
-       "      <td>0.162650</td>\n",
-       "      <td>0.154400</td>\n",
-       "      <td>0.144275</td>\n",
+       "      <th>...</th>\n",
        "      <td>...</td>\n",
-       "      <td>0.143763</td>\n",
-       "      <td>0.151444</td>\n",
-       "      <td>0.157156</td>\n",
-       "      <td>0.153878</td>\n",
-       "      <td>0.138533</td>\n",
-       "      <td>0.138200</td>\n",
-       "      <td>0.138525</td>\n",
-       "      <td>0.136667</td>\n",
-       "      <td>0.135871</td>\n",
-       "      <td>0.139700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.5</th>\n",
-       "      <td>0.138550</td>\n",
-       "      <td>0.137725</td>\n",
-       "      <td>0.135490</td>\n",
-       "      <td>0.134900</td>\n",
-       "      <td>0.136400</td>\n",
-       "      <td>0.137218</td>\n",
-       "      <td>0.139250</td>\n",
-       "      <td>0.156625</td>\n",
-       "      <td>0.159414</td>\n",
-       "      <td>0.147300</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.149229</td>\n",
-       "      <td>0.158000</td>\n",
-       "      <td>0.151414</td>\n",
-       "      <td>0.143550</td>\n",
-       "      <td>0.135358</td>\n",
-       "      <td>0.136343</td>\n",
-       "      <td>0.134927</td>\n",
-       "      <td>0.134337</td>\n",
-       "      <td>0.136756</td>\n",
-       "      <td>0.136367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.6</th>\n",
-       "      <td>0.137725</td>\n",
-       "      <td>0.134624</td>\n",
-       "      <td>0.135183</td>\n",
-       "      <td>0.134150</td>\n",
-       "      <td>0.135617</td>\n",
-       "      <td>0.134246</td>\n",
-       "      <td>0.135311</td>\n",
-       "      <td>0.144350</td>\n",
-       "      <td>0.158150</td>\n",
-       "      <td>0.156129</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.157864</td>\n",
-       "      <td>0.157836</td>\n",
-       "      <td>0.145880</td>\n",
-       "      <td>0.135780</td>\n",
-       "      <td>0.134319</td>\n",
-       "      <td>0.134755</td>\n",
-       "      <td>0.134533</td>\n",
-       "      <td>0.135633</td>\n",
-       "      <td>0.135900</td>\n",
-       "      <td>0.134450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.7</th>\n",
-       "      <td>0.135840</td>\n",
-       "      <td>0.135129</td>\n",
-       "      <td>0.132957</td>\n",
-       "      <td>0.132900</td>\n",
-       "      <td>0.133153</td>\n",
-       "      <td>0.132860</td>\n",
-       "      <td>0.135122</td>\n",
-       "      <td>0.136907</td>\n",
-       "      <td>0.148817</td>\n",
-       "      <td>0.159080</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.161543</td>\n",
-       "      <td>0.151389</td>\n",
-       "      <td>0.139600</td>\n",
-       "      <td>0.134067</td>\n",
-       "      <td>0.132920</td>\n",
-       "      <td>0.133156</td>\n",
-       "      <td>0.133244</td>\n",
-       "      <td>0.133837</td>\n",
-       "      <td>0.132917</td>\n",
-       "      <td>0.134400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.8</th>\n",
-       "      <td>0.146100</td>\n",
-       "      <td>0.139309</td>\n",
-       "      <td>0.138750</td>\n",
-       "      <td>0.138678</td>\n",
-       "      <td>0.136657</td>\n",
-       "      <td>0.135780</td>\n",
-       "      <td>0.135900</td>\n",
-       "      <td>0.133218</td>\n",
-       "      <td>0.140078</td>\n",
-       "      <td>0.150836</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.153625</td>\n",
-       "      <td>0.144011</td>\n",
-       "      <td>0.136418</td>\n",
-       "      <td>0.137729</td>\n",
-       "      <td>0.137500</td>\n",
-       "      <td>0.138112</td>\n",
-       "      <td>0.141614</td>\n",
-       "      <td>0.137290</td>\n",
-       "      <td>0.140292</td>\n",
-       "      <td>0.144650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.9</th>\n",
-       "      <td>0.138650</td>\n",
-       "      <td>0.137057</td>\n",
-       "      <td>0.140111</td>\n",
-       "      <td>0.136422</td>\n",
-       "      <td>0.135869</td>\n",
-       "      <td>0.136715</td>\n",
-       "      <td>0.138400</td>\n",
-       "      <td>0.133900</td>\n",
-       "      <td>0.140856</td>\n",
-       "      <td>0.138470</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.142667</td>\n",
-       "      <td>0.134730</td>\n",
-       "      <td>0.136569</td>\n",
-       "      <td>0.137010</td>\n",
-       "      <td>0.136192</td>\n",
-       "      <td>0.137589</td>\n",
-       "      <td>0.139643</td>\n",
-       "      <td>0.138133</td>\n",
-       "      <td>0.135671</td>\n",
-       "      <td>0.143737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.0</th>\n",
-       "      <td>0.140580</td>\n",
-       "      <td>0.132220</td>\n",
-       "      <td>0.132673</td>\n",
-       "      <td>0.132340</td>\n",
-       "      <td>0.132370</td>\n",
-       "      <td>0.132330</td>\n",
-       "      <td>0.132350</td>\n",
-       "      <td>0.132364</td>\n",
-       "      <td>0.132364</td>\n",
-       "      <td>0.132875</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.132560</td>\n",
-       "      <td>0.132367</td>\n",
-       "      <td>0.132367</td>\n",
-       "      <td>0.132330</td>\n",
-       "      <td>0.132321</td>\n",
-       "      <td>0.132323</td>\n",
-       "      <td>0.132336</td>\n",
-       "      <td>0.132500</td>\n",
-       "      <td>0.132250</td>\n",
-       "      <td>0.131733</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.1</th>\n",
-       "      <td>0.140850</td>\n",
-       "      <td>0.133550</td>\n",
-       "      <td>0.132243</td>\n",
-       "      <td>0.133160</td>\n",
-       "      <td>0.132200</td>\n",
-       "      <td>0.135089</td>\n",
-       "      <td>0.133630</td>\n",
-       "      <td>0.135355</td>\n",
-       "      <td>0.136069</td>\n",
-       "      <td>0.135850</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.134755</td>\n",
-       "      <td>0.134214</td>\n",
-       "      <td>0.135983</td>\n",
-       "      <td>0.134942</td>\n",
-       "      <td>0.132553</td>\n",
-       "      <td>0.132840</td>\n",
-       "      <td>0.133667</td>\n",
-       "      <td>0.132217</td>\n",
-       "      <td>0.132188</td>\n",
-       "      <td>0.140175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.2</th>\n",
-       "      <td>0.141883</td>\n",
-       "      <td>0.138843</td>\n",
-       "      <td>0.137400</td>\n",
-       "      <td>0.138456</td>\n",
-       "      <td>0.135829</td>\n",
-       "      <td>0.134738</td>\n",
-       "      <td>0.133143</td>\n",
-       "      <td>0.132936</td>\n",
-       "      <td>0.133822</td>\n",
-       "      <td>0.134731</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.136450</td>\n",
-       "      <td>0.139473</td>\n",
-       "      <td>0.134975</td>\n",
-       "      <td>0.135812</td>\n",
-       "      <td>0.136360</td>\n",
-       "      <td>0.135514</td>\n",
-       "      <td>0.136362</td>\n",
-       "      <td>0.137678</td>\n",
-       "      <td>0.142022</td>\n",
-       "      <td>0.136033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.3</th>\n",
-       "      <td>0.139400</td>\n",
-       "      <td>0.136378</td>\n",
-       "      <td>0.137167</td>\n",
-       "      <td>0.134940</td>\n",
-       "      <td>0.132460</td>\n",
-       "      <td>0.136362</td>\n",
-       "      <td>0.133274</td>\n",
-       "      <td>0.132863</td>\n",
-       "      <td>0.136533</td>\n",
-       "      <td>0.130312</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.131400</td>\n",
-       "      <td>0.131879</td>\n",
-       "      <td>0.130691</td>\n",
-       "      <td>0.131740</td>\n",
-       "      <td>0.132938</td>\n",
-       "      <td>0.134318</td>\n",
-       "      <td>0.136760</td>\n",
-       "      <td>0.138513</td>\n",
-       "      <td>0.140400</td>\n",
-       "      <td>0.146788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.4</th>\n",
-       "      <td>0.141000</td>\n",
-       "      <td>0.130790</td>\n",
-       "      <td>0.129220</td>\n",
-       "      <td>0.129329</td>\n",
-       "      <td>0.130916</td>\n",
-       "      <td>0.129267</td>\n",
-       "      <td>0.128655</td>\n",
-       "      <td>0.132400</td>\n",
-       "      <td>0.128681</td>\n",
-       "      <td>0.128758</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.131280</td>\n",
-       "      <td>0.128478</td>\n",
-       "      <td>0.128667</td>\n",
-       "      <td>0.128831</td>\n",
-       "      <td>0.133727</td>\n",
-       "      <td>0.133914</td>\n",
-       "      <td>0.130500</td>\n",
-       "      <td>0.128700</td>\n",
-       "      <td>0.128457</td>\n",
-       "      <td>0.136000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.5</th>\n",
-       "      <td>0.133933</td>\n",
-       "      <td>0.140282</td>\n",
-       "      <td>0.135745</td>\n",
-       "      <td>0.147691</td>\n",
-       "      <td>0.127075</td>\n",
-       "      <td>0.134500</td>\n",
-       "      <td>0.130312</td>\n",
-       "      <td>0.146278</td>\n",
-       "      <td>0.128320</td>\n",
-       "      <td>0.142617</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.135367</td>\n",
-       "      <td>0.147464</td>\n",
-       "      <td>0.135347</td>\n",
-       "      <td>0.135480</td>\n",
-       "      <td>0.131614</td>\n",
-       "      <td>0.128662</td>\n",
-       "      <td>0.140411</td>\n",
-       "      <td>0.134522</td>\n",
-       "      <td>0.137387</td>\n",
-       "      <td>0.137213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.6</th>\n",
-       "      <td>0.157160</td>\n",
-       "      <td>0.150125</td>\n",
-       "      <td>0.190522</td>\n",
-       "      <td>0.163414</td>\n",
-       "      <td>0.153340</td>\n",
-       "      <td>0.182489</td>\n",
-       "      <td>0.146633</td>\n",
-       "      <td>0.174283</td>\n",
-       "      <td>0.164600</td>\n",
-       "      <td>0.156060</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.167818</td>\n",
-       "      <td>0.175760</td>\n",
-       "      <td>0.172300</td>\n",
-       "      <td>0.170070</td>\n",
-       "      <td>0.172709</td>\n",
-       "      <td>0.160782</td>\n",
-       "      <td>0.165338</td>\n",
-       "      <td>0.150207</td>\n",
-       "      <td>0.169028</td>\n",
-       "      <td>0.172525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.7</th>\n",
-       "      <td>0.212600</td>\n",
-       "      <td>0.203550</td>\n",
-       "      <td>0.182613</td>\n",
-       "      <td>0.217033</td>\n",
-       "      <td>0.196530</td>\n",
-       "      <td>0.192733</td>\n",
-       "      <td>0.196800</td>\n",
-       "      <td>0.196678</td>\n",
-       "      <td>0.194400</td>\n",
-       "      <td>0.193862</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.207025</td>\n",
-       "      <td>0.213778</td>\n",
-       "      <td>0.199458</td>\n",
-       "      <td>0.195767</td>\n",
-       "      <td>0.202582</td>\n",
-       "      <td>0.193050</td>\n",
-       "      <td>0.203717</td>\n",
-       "      <td>0.188890</td>\n",
-       "      <td>0.187186</td>\n",
-       "      <td>0.219900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.8</th>\n",
-       "      <td>0.258014</td>\n",
-       "      <td>0.226800</td>\n",
-       "      <td>0.214507</td>\n",
-       "      <td>0.220812</td>\n",
-       "      <td>0.212843</td>\n",
-       "      <td>0.213190</td>\n",
-       "      <td>0.214863</td>\n",
-       "      <td>0.214723</td>\n",
-       "      <td>0.225433</td>\n",
-       "      <td>0.221414</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.258750</td>\n",
-       "      <td>0.219029</td>\n",
-       "      <td>0.216213</td>\n",
-       "      <td>0.218565</td>\n",
-       "      <td>0.214589</td>\n",
-       "      <td>0.215342</td>\n",
-       "      <td>0.212015</td>\n",
-       "      <td>0.208669</td>\n",
-       "      <td>0.218762</td>\n",
-       "      <td>0.209350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.9</th>\n",
-       "      <td>0.312460</td>\n",
-       "      <td>0.259640</td>\n",
-       "      <td>0.229650</td>\n",
-       "      <td>0.263078</td>\n",
-       "      <td>0.252018</td>\n",
-       "      <td>0.237225</td>\n",
-       "      <td>0.237745</td>\n",
-       "      <td>0.297200</td>\n",
-       "      <td>0.298883</td>\n",
-       "      <td>0.219567</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.281725</td>\n",
-       "      <td>0.263557</td>\n",
-       "      <td>0.288811</td>\n",
-       "      <td>0.296427</td>\n",
-       "      <td>0.269145</td>\n",
-       "      <td>0.287975</td>\n",
-       "      <td>0.327073</td>\n",
-       "      <td>0.329788</td>\n",
-       "      <td>0.274791</td>\n",
-       "      <td>0.319000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>0.261700</td>\n",
-       "      <td>0.265618</td>\n",
-       "      <td>0.158400</td>\n",
-       "      <td>0.276025</td>\n",
-       "      <td>0.353650</td>\n",
-       "      <td>0.274587</td>\n",
-       "      <td>0.289418</td>\n",
-       "      <td>0.250657</td>\n",
-       "      <td>0.247240</td>\n",
-       "      <td>0.233933</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.230525</td>\n",
-       "      <td>0.312200</td>\n",
-       "      <td>0.261683</td>\n",
-       "      <td>0.321733</td>\n",
-       "      <td>0.295550</td>\n",
-       "      <td>0.319608</td>\n",
-       "      <td>0.270450</td>\n",
-       "      <td>0.303856</td>\n",
-       "      <td>0.355400</td>\n",
-       "      <td>0.277650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.1</th>\n",
-       "      <td>0.141975</td>\n",
-       "      <td>0.146789</td>\n",
-       "      <td>0.188287</td>\n",
-       "      <td>0.179920</td>\n",
-       "      <td>0.178042</td>\n",
-       "      <td>0.198750</td>\n",
-       "      <td>0.169862</td>\n",
-       "      <td>0.183632</td>\n",
-       "      <td>0.133243</td>\n",
-       "      <td>0.188200</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.259920</td>\n",
-       "      <td>0.189321</td>\n",
-       "      <td>0.252600</td>\n",
-       "      <td>0.117550</td>\n",
-       "      <td>0.130433</td>\n",
-       "      <td>0.203973</td>\n",
-       "      <td>0.167662</td>\n",
-       "      <td>0.189260</td>\n",
-       "      <td>0.194700</td>\n",
-       "      <td>0.145700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.2</th>\n",
-       "      <td>0.208650</td>\n",
-       "      <td>0.172833</td>\n",
-       "      <td>0.240123</td>\n",
-       "      <td>0.270567</td>\n",
-       "      <td>0.174783</td>\n",
-       "      <td>0.187754</td>\n",
-       "      <td>0.257860</td>\n",
-       "      <td>0.179833</td>\n",
-       "      <td>0.161525</td>\n",
-       "      <td>0.129600</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.233350</td>\n",
-       "      <td>0.357600</td>\n",
-       "      <td>0.230143</td>\n",
-       "      <td>0.198875</td>\n",
-       "      <td>0.153750</td>\n",
-       "      <td>0.207388</td>\n",
-       "      <td>0.251690</td>\n",
-       "      <td>0.198325</td>\n",
-       "      <td>0.178300</td>\n",
-       "      <td>0.300800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.3</th>\n",
-       "      <td>0.488733</td>\n",
-       "      <td>0.894550</td>\n",
-       "      <td>0.438914</td>\n",
-       "      <td>0.666500</td>\n",
-       "      <td>0.436417</td>\n",
-       "      <td>0.223957</td>\n",
-       "      <td>0.315433</td>\n",
-       "      <td>0.287644</td>\n",
-       "      <td>0.346513</td>\n",
-       "      <td>0.350088</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.347413</td>\n",
-       "      <td>0.602767</td>\n",
-       "      <td>0.622100</td>\n",
-       "      <td>0.784133</td>\n",
-       "      <td>0.214943</td>\n",
-       "      <td>0.308667</td>\n",
-       "      <td>0.246850</td>\n",
-       "      <td>0.686620</td>\n",
-       "      <td>0.189040</td>\n",
-       "      <td>0.310000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.4</th>\n",
-       "      <td>0.104350</td>\n",
-       "      <td>0.507614</td>\n",
-       "      <td>1.530550</td>\n",
-       "      <td>0.909633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.779233</td>\n",
-       "      <td>0.098667</td>\n",
-       "      <td>1.691500</td>\n",
-       "      <td>0.099500</td>\n",
-       "      <td>0.609345</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.267200</td>\n",
-       "      <td>0.247160</td>\n",
-       "      <td>0.756825</td>\n",
-       "      <td>0.685763</td>\n",
-       "      <td>0.519725</td>\n",
-       "      <td>0.519360</td>\n",
-       "      <td>1.221350</td>\n",
-       "      <td>0.127637</td>\n",
-       "      <td>0.169967</td>\n",
-       "      <td>0.296950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4.5</th>\n",
+       "      <th>9.6</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.09270</td>\n",
+       "      <td>0.09775</td>\n",
+       "      <td>0.099525</td>\n",
+       "      <td>0.09825</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0926</td>\n",
+       "      <td>0.09710</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.104690</td>\n",
-       "      <td>0.118533</td>\n",
-       "      <td>0.121583</td>\n",
-       "      <td>0.092800</td>\n",
-       "      <td>0.092717</td>\n",
-       "      <td>0.100013</td>\n",
-       "      <td>0.100217</td>\n",
-       "      <td>0.103375</td>\n",
-       "      <td>0.108060</td>\n",
+       "      <td>0.0934</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.098950</td>\n",
-       "      <td>0.097314</td>\n",
-       "      <td>0.099140</td>\n",
-       "      <td>0.104000</td>\n",
-       "      <td>0.114250</td>\n",
-       "      <td>0.098533</td>\n",
-       "      <td>0.089100</td>\n",
-       "      <td>0.176213</td>\n",
-       "      <td>0.116700</td>\n",
-       "      <td>0.106100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.6</th>\n",
-       "      <td>0.104450</td>\n",
-       "      <td>0.103500</td>\n",
-       "      <td>0.095200</td>\n",
-       "      <td>0.096600</td>\n",
-       "      <td>0.100317</td>\n",
-       "      <td>0.097909</td>\n",
-       "      <td>0.099567</td>\n",
-       "      <td>0.095550</td>\n",
-       "      <td>0.101467</td>\n",
-       "      <td>0.096550</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.101150</td>\n",
-       "      <td>0.103700</td>\n",
-       "      <td>0.102917</td>\n",
-       "      <td>0.089000</td>\n",
-       "      <td>0.102150</td>\n",
-       "      <td>0.109400</td>\n",
-       "      <td>0.100788</td>\n",
-       "      <td>0.102500</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.09000</td>\n",
+       "      <td>0.1005</td>\n",
+       "      <td>0.09510</td>\n",
+       "      <td>0.1005</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0960</td>\n",
+       "      <td>0.09185</td>\n",
+       "      <td>0.094067</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4.7</th>\n",
-       "      <td>0.094300</td>\n",
-       "      <td>0.101400</td>\n",
-       "      <td>0.099475</td>\n",
+       "      <th>9.7</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.098700</td>\n",
-       "      <td>0.095050</td>\n",
-       "      <td>0.095020</td>\n",
-       "      <td>0.095925</td>\n",
-       "      <td>0.099850</td>\n",
+       "      <td>0.09340</td>\n",
+       "      <td>0.09075</td>\n",
+       "      <td>0.0900</td>\n",
+       "      <td>0.08810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.098260</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.096500</td>\n",
-       "      <td>0.096100</td>\n",
-       "      <td>0.090350</td>\n",
-       "      <td>0.096900</td>\n",
-       "      <td>0.087700</td>\n",
-       "      <td>0.097950</td>\n",
-       "      <td>0.089450</td>\n",
-       "      <td>0.097233</td>\n",
-       "      <td>0.094300</td>\n",
+       "      <td>0.0912</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.09350</td>\n",
+       "      <td>0.0962</td>\n",
+       "      <td>0.0971</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.09540</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4.8</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.094333</td>\n",
-       "      <td>0.097750</td>\n",
-       "      <td>0.099525</td>\n",
-       "      <td>0.094929</td>\n",
-       "      <td>0.089000</td>\n",
-       "      <td>0.092600</td>\n",
-       "      <td>0.095250</td>\n",
-       "      <td>0.095100</td>\n",
-       "      <td>0.094150</td>\n",
+       "      <th>9.8</th>\n",
+       "      <td>0.0922</td>\n",
+       "      <td>0.09230</td>\n",
+       "      <td>0.09660</td>\n",
+       "      <td>0.089067</td>\n",
+       "      <td>0.09260</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0802</td>\n",
+       "      <td>0.09195</td>\n",
+       "      <td>0.090867</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.091200</td>\n",
-       "      <td>0.090000</td>\n",
-       "      <td>0.100500</td>\n",
-       "      <td>0.095100</td>\n",
-       "      <td>0.098800</td>\n",
-       "      <td>0.097100</td>\n",
-       "      <td>0.096000</td>\n",
-       "      <td>0.094133</td>\n",
-       "      <td>0.094067</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.07670</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.08775</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0920</td>\n",
+       "      <td>0.08030</td>\n",
+       "      <td>0.100300</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4.9</th>\n",
-       "      <td>0.092200</td>\n",
-       "      <td>0.092300</td>\n",
-       "      <td>0.091700</td>\n",
-       "      <td>0.088420</td>\n",
-       "      <td>0.092600</td>\n",
-       "      <td>0.089000</td>\n",
-       "      <td>0.085100</td>\n",
-       "      <td>0.090667</td>\n",
-       "      <td>0.090650</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>9.9</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.09080</td>\n",
+       "      <td>0.08490</td>\n",
+       "      <td>0.076200</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.08550</td>\n",
+       "      <td>0.0900</td>\n",
+       "      <td>0.09840</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.082850</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.089667</td>\n",
-       "      <td>0.093100</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.092000</td>\n",
-       "      <td>0.087850</td>\n",
-       "      <td>0.091650</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.3018</td>\n",
+       "      <td>0.08900</td>\n",
+       "      <td>0.0889</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0900</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.08160</td>\n",
+       "      <td>0.088767</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5.0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.141100</td>\n",
-       "      <td>0.076200</td>\n",
+       "      <th>10.0</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.19140</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.119100</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.17850</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.178500</td>\n",
-       "      <td>0.090000</td>\n",
-       "      <td>0.098400</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.301800</td>\n",
-       "      <td>0.104250</td>\n",
-       "      <td>0.103567</td>\n",
-       "      <td>0.085500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.081600</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.10425</td>\n",
+       "      <td>0.1109</td>\n",
+       "      <td>0.08550</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.081500</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>36 rows × 181 columns</p>\n",
+       "<p>70 rows × 181 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "phi     -90.0     -89.0     -88.0     -87.0     -86.0     -85.0     -84.0  \\\n",
-       "eta                                                                         \n",
-       "1.5  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-       "1.6  0.000000  0.745900  0.000000  0.746800  0.000000  0.000000  0.000000   \n",
-       "1.7  0.000000  1.178400  0.295200  0.743500  0.379000  0.614800  1.693300   \n",
-       "1.8  0.381100  0.386050  0.243450  0.168900  0.000000  0.229833  0.229900   \n",
-       "1.9  0.153000  0.000000  0.142200  0.239467  0.295500  0.150900  0.273500   \n",
-       "2.0  0.151000  0.137790  0.139500  0.141687  0.162550  0.160067  0.161450   \n",
-       "2.1  0.132200  0.137630  0.147300  0.136700  0.139863  0.165500  0.150780   \n",
-       "2.2  0.138267  0.136160  0.137342  0.136750  0.138395  0.161375  0.155864   \n",
-       "2.3  0.140120  0.135150  0.136392  0.136000  0.135283  0.138733  0.162330   \n",
-       "2.4  0.146343  0.137150  0.140820  0.137143  0.134425  0.138600  0.153840   \n",
-       "2.5  0.138550  0.137725  0.135490  0.134900  0.136400  0.137218  0.139250   \n",
-       "2.6  0.137725  0.134624  0.135183  0.134150  0.135617  0.134246  0.135311   \n",
-       "2.7  0.135840  0.135129  0.132957  0.132900  0.133153  0.132860  0.135122   \n",
-       "2.8  0.146100  0.139309  0.138750  0.138678  0.136657  0.135780  0.135900   \n",
-       "2.9  0.138650  0.137057  0.140111  0.136422  0.135869  0.136715  0.138400   \n",
-       "3.0  0.140580  0.132220  0.132673  0.132340  0.132370  0.132330  0.132350   \n",
-       "3.1  0.140850  0.133550  0.132243  0.133160  0.132200  0.135089  0.133630   \n",
-       "3.2  0.141883  0.138843  0.137400  0.138456  0.135829  0.134738  0.133143   \n",
-       "3.3  0.139400  0.136378  0.137167  0.134940  0.132460  0.136362  0.133274   \n",
-       "3.4  0.141000  0.130790  0.129220  0.129329  0.130916  0.129267  0.128655   \n",
-       "3.5  0.133933  0.140282  0.135745  0.147691  0.127075  0.134500  0.130312   \n",
-       "3.6  0.157160  0.150125  0.190522  0.163414  0.153340  0.182489  0.146633   \n",
-       "3.7  0.212600  0.203550  0.182613  0.217033  0.196530  0.192733  0.196800   \n",
-       "3.8  0.258014  0.226800  0.214507  0.220812  0.212843  0.213190  0.214863   \n",
-       "3.9  0.312460  0.259640  0.229650  0.263078  0.252018  0.237225  0.237745   \n",
-       "4.0  0.261700  0.265618  0.158400  0.276025  0.353650  0.274587  0.289418   \n",
-       "4.1  0.141975  0.146789  0.188287  0.179920  0.178042  0.198750  0.169862   \n",
-       "4.2  0.208650  0.172833  0.240123  0.270567  0.174783  0.187754  0.257860   \n",
-       "4.3  0.488733  0.894550  0.438914  0.666500  0.436417  0.223957  0.315433   \n",
-       "4.4  0.104350  0.507614  1.530550  0.909633  0.000000  0.779233  0.098667   \n",
-       "4.5  0.000000  0.104690  0.118533  0.121583  0.092800  0.092717  0.100013   \n",
-       "4.6  0.104450  0.103500  0.095200  0.096600  0.100317  0.097909  0.099567   \n",
-       "4.7  0.094300  0.101400  0.099475  0.098700  0.095050  0.095020  0.095925   \n",
-       "4.8  0.000000  0.094333  0.097750  0.099525  0.094929  0.089000  0.092600   \n",
-       "4.9  0.092200  0.092300  0.091700  0.088420  0.092600  0.089000  0.085100   \n",
-       "5.0  0.000000  0.141100  0.076200  0.119100  0.000000  0.178500  0.090000   \n",
+       "phi    -90.0    -89.0    -88.0     -87.0    -86.0    -85.0   -84.0    -83.0  \\\n",
+       "eta                                                                           \n",
+       "3.1   0.0000  0.00000  0.00000  0.000000  0.00000  0.00000  0.0000  0.00000   \n",
+       "3.2   0.0000  0.00000  0.00000  0.000000  0.00000  0.00000  0.0000  0.00000   \n",
+       "3.3   0.0000  0.96215  0.00000  0.746800  0.00000  0.00000  1.7263  0.00000   \n",
+       "3.4   0.0000  0.00000  0.00000  0.743500  0.37900  0.11090  1.6273  0.00000   \n",
+       "3.5   0.0000  0.38280  0.33605  0.110300  0.00000  1.11870  0.0000  0.23330   \n",
+       "...      ...      ...      ...       ...      ...      ...     ...      ...   \n",
+       "9.6   0.0000  0.09270  0.09775  0.099525  0.09825  0.00000  0.0926  0.09710   \n",
+       "9.7   0.0000  0.00000  0.00000  0.098700  0.09340  0.09075  0.0900  0.08810   \n",
+       "9.8   0.0922  0.09230  0.09660  0.089067  0.09260  0.00000  0.0802  0.09195   \n",
+       "9.9   0.0000  0.09080  0.08490  0.076200  0.00000  0.08550  0.0900  0.09840   \n",
+       "10.0  0.0000  0.19140  0.00000  0.119100  0.00000  0.17850  0.0000  0.00000   \n",
        "\n",
-       "phi     -83.0     -82.0     -81.0  ...      81.0      82.0      83.0  \\\n",
-       "eta                                ...                                 \n",
-       "1.5  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
-       "1.6  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
-       "1.7  0.000000  0.930075  0.111500  ...  0.381300  0.000000  0.000000   \n",
-       "1.8  0.390667  0.165900  0.327267  ...  0.223500  0.236243  0.883980   \n",
-       "1.9  0.161000  0.867600  0.000000  ...  0.220200  0.171267  0.163900   \n",
-       "2.0  0.149060  0.169400  0.176289  ...  0.172440  0.176475  0.153450   \n",
-       "2.1  0.149290  0.152229  0.168450  ...  0.165614  0.155900  0.150020   \n",
-       "2.2  0.150350  0.148888  0.153114  ...  0.155750  0.149120  0.148875   \n",
-       "2.3  0.156182  0.147925  0.145092  ...  0.159367  0.148900  0.155467   \n",
-       "2.4  0.162650  0.154400  0.144275  ...  0.143763  0.151444  0.157156   \n",
-       "2.5  0.156625  0.159414  0.147300  ...  0.149229  0.158000  0.151414   \n",
-       "2.6  0.144350  0.158150  0.156129  ...  0.157864  0.157836  0.145880   \n",
-       "2.7  0.136907  0.148817  0.159080  ...  0.161543  0.151389  0.139600   \n",
-       "2.8  0.133218  0.140078  0.150836  ...  0.153625  0.144011  0.136418   \n",
-       "2.9  0.133900  0.140856  0.138470  ...  0.142667  0.134730  0.136569   \n",
-       "3.0  0.132364  0.132364  0.132875  ...  0.132560  0.132367  0.132367   \n",
-       "3.1  0.135355  0.136069  0.135850  ...  0.134755  0.134214  0.135983   \n",
-       "3.2  0.132936  0.133822  0.134731  ...  0.136450  0.139473  0.134975   \n",
-       "3.3  0.132863  0.136533  0.130312  ...  0.131400  0.131879  0.130691   \n",
-       "3.4  0.132400  0.128681  0.128758  ...  0.131280  0.128478  0.128667   \n",
-       "3.5  0.146278  0.128320  0.142617  ...  0.135367  0.147464  0.135347   \n",
-       "3.6  0.174283  0.164600  0.156060  ...  0.167818  0.175760  0.172300   \n",
-       "3.7  0.196678  0.194400  0.193862  ...  0.207025  0.213778  0.199458   \n",
-       "3.8  0.214723  0.225433  0.221414  ...  0.258750  0.219029  0.216213   \n",
-       "3.9  0.297200  0.298883  0.219567  ...  0.281725  0.263557  0.288811   \n",
-       "4.0  0.250657  0.247240  0.233933  ...  0.230525  0.312200  0.261683   \n",
-       "4.1  0.183632  0.133243  0.188200  ...  0.259920  0.189321  0.252600   \n",
-       "4.2  0.179833  0.161525  0.129600  ...  0.233350  0.357600  0.230143   \n",
-       "4.3  0.287644  0.346513  0.350088  ...  0.347413  0.602767  0.622100   \n",
-       "4.4  1.691500  0.099500  0.609345  ...  0.267200  0.247160  0.756825   \n",
-       "4.5  0.100217  0.103375  0.108060  ...  0.098950  0.097314  0.099140   \n",
-       "4.6  0.095550  0.101467  0.096550  ...  0.000000  0.101150  0.103700   \n",
-       "4.7  0.099850  0.000000  0.098260  ...  0.000000  0.096500  0.096100   \n",
-       "4.8  0.095250  0.095100  0.094150  ...  0.091200  0.090000  0.100500   \n",
-       "4.9  0.090667  0.090650  0.000000  ...  0.000000  0.082850  0.000000   \n",
-       "5.0  0.098400  0.000000  0.000000  ...  0.301800  0.104250  0.103567   \n",
+       "phi      -82.0   -81.0  ...    81.0     82.0    83.0     84.0    85.0    86.0  \\\n",
+       "eta                     ...                                                     \n",
+       "3.1   0.000000  0.0000  ...  0.0000  0.00000  0.0000  0.00000  0.0000  0.0000   \n",
+       "3.2   0.000000  0.0000  ...  0.0000  0.00000  0.0000  0.00000  0.0000  0.0000   \n",
+       "3.3   0.381400  0.0000  ...  0.3813  0.00000  0.0000  0.00000  0.0000  0.0000   \n",
+       "3.4   0.380200  0.1115  ...  0.0000  0.00000  0.0000  0.38025  1.1143  0.7429   \n",
+       "3.5   1.479350  0.0000  ...  0.1458  0.39860  0.0000  0.39800  0.0000  0.7175   \n",
+       "...        ...     ...  ...     ...      ...     ...      ...     ...     ...   \n",
+       "9.6   0.000000  0.0934  ...  0.0000  0.09000  0.1005  0.09510  0.1005  0.0000   \n",
+       "9.7   0.000000  0.0000  ...  0.0912  0.00000  0.0000  0.09350  0.0962  0.0971   \n",
+       "9.8   0.090867  0.0000  ...  0.0000  0.07670  0.0000  0.08775  0.0000  0.0000   \n",
+       "9.9   0.090000  0.0000  ...  0.3018  0.08900  0.0889  0.00000  0.0900  0.0000   \n",
+       "10.0  0.000000  0.0000  ...  0.0000  0.10425  0.1109  0.08550  0.0000  0.0000   \n",
        "\n",
-       "phi      84.0      85.0      86.0      87.0      88.0      89.0      90.0  \n",
-       "eta                                                                        \n",
-       "1.5  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-       "1.6  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-       "1.7  0.380250  1.114300  0.730200  0.420600  0.000000  0.377950  0.000000  \n",
-       "1.8  0.270350  1.366700  0.160850  0.000000  0.272100  0.234500  0.124500  \n",
-       "1.9  0.151325  0.154617  0.251200  0.139100  0.191800  0.224867  0.144000  \n",
-       "2.0  0.152857  0.178971  0.154686  0.141500  0.140043  0.141740  0.134250  \n",
-       "2.1  0.148483  0.160091  0.141550  0.143433  0.137018  0.137937  0.137000  \n",
-       "2.2  0.158500  0.159117  0.139737  0.136467  0.136900  0.136238  0.146150  \n",
-       "2.3  0.160300  0.145629  0.145200  0.139894  0.141271  0.138073  0.141620  \n",
-       "2.4  0.153878  0.138533  0.138200  0.138525  0.136667  0.135871  0.139700  \n",
-       "2.5  0.143550  0.135358  0.136343  0.134927  0.134337  0.136756  0.136367  \n",
-       "2.6  0.135780  0.134319  0.134755  0.134533  0.135633  0.135900  0.134450  \n",
-       "2.7  0.134067  0.132920  0.133156  0.133244  0.133837  0.132917  0.134400  \n",
-       "2.8  0.137729  0.137500  0.138112  0.141614  0.137290  0.140292  0.144650  \n",
-       "2.9  0.137010  0.136192  0.137589  0.139643  0.138133  0.135671  0.143737  \n",
-       "3.0  0.132330  0.132321  0.132323  0.132336  0.132500  0.132250  0.131733  \n",
-       "3.1  0.134942  0.132553  0.132840  0.133667  0.132217  0.132188  0.140175  \n",
-       "3.2  0.135812  0.136360  0.135514  0.136362  0.137678  0.142022  0.136033  \n",
-       "3.3  0.131740  0.132938  0.134318  0.136760  0.138513  0.140400  0.146788  \n",
-       "3.4  0.128831  0.133727  0.133914  0.130500  0.128700  0.128457  0.136000  \n",
-       "3.5  0.135480  0.131614  0.128662  0.140411  0.134522  0.137387  0.137213  \n",
-       "3.6  0.170070  0.172709  0.160782  0.165338  0.150207  0.169028  0.172525  \n",
-       "3.7  0.195767  0.202582  0.193050  0.203717  0.188890  0.187186  0.219900  \n",
-       "3.8  0.218565  0.214589  0.215342  0.212015  0.208669  0.218762  0.209350  \n",
-       "3.9  0.296427  0.269145  0.287975  0.327073  0.329788  0.274791  0.319000  \n",
-       "4.0  0.321733  0.295550  0.319608  0.270450  0.303856  0.355400  0.277650  \n",
-       "4.1  0.117550  0.130433  0.203973  0.167662  0.189260  0.194700  0.145700  \n",
-       "4.2  0.198875  0.153750  0.207388  0.251690  0.198325  0.178300  0.300800  \n",
-       "4.3  0.784133  0.214943  0.308667  0.246850  0.686620  0.189040  0.310000  \n",
-       "4.4  0.685763  0.519725  0.519360  1.221350  0.127637  0.169967  0.296950  \n",
-       "4.5  0.104000  0.114250  0.098533  0.089100  0.176213  0.116700  0.106100  \n",
-       "4.6  0.102917  0.089000  0.102150  0.109400  0.100788  0.102500  0.000000  \n",
-       "4.7  0.090350  0.096900  0.087700  0.097950  0.089450  0.097233  0.094300  \n",
-       "4.8  0.095100  0.098800  0.097100  0.096000  0.094133  0.094067  0.000000  \n",
-       "4.9  0.089667  0.093100  0.000000  0.092000  0.087850  0.091650  0.000000  \n",
-       "5.0  0.085500  0.000000  0.000000  0.000000  0.081600  0.081500  0.000000  \n",
+       "phi     87.0     88.0      89.0   90.0  \n",
+       "eta                                     \n",
+       "3.1   0.0000  0.00000  0.000000    0.0  \n",
+       "3.2   0.0000  0.00000  0.000000    0.0  \n",
+       "3.3   0.0000  0.00000  0.000000    0.0  \n",
+       "3.4   0.1109  0.00000  0.378400    0.0  \n",
+       "3.5   0.7303  0.00000  0.243900    0.0  \n",
+       "...      ...      ...       ...    ...  \n",
+       "9.6   0.0960  0.09185  0.094067    0.0  \n",
+       "9.7   0.0000  0.09540  0.000000    0.0  \n",
+       "9.8   0.0920  0.08030  0.100300    0.0  \n",
+       "9.9   0.0000  0.08160  0.088767    0.0  \n",
+       "10.0  0.0000  0.00000  0.081500    0.0  \n",
        "\n",
-       "[36 rows x 181 columns]"
+       "[70 rows x 181 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1625,14 +948,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 2100x700 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1640,18 +963,19 @@
     }
    ],
    "source": [
+    "fig = plt.figure(figsize=(21, 7))\n",
     "ax = sns.heatmap(\n",
     "    df_pivoted,\n",
     "    robust=True,\n",
-    "    square=False,\n",
+    "    square=True,\n",
     "    cmap=colormaps[\"rainbow\"],\n",
     "    xticklabels=False,\n",
     "    yticklabels=False,\n",
     "    vmax=0.7,\n",
     ")\n",
-    "ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
-    "ax.set_xticks([39, 89, 139],\n",
-    "              [-100, 0, 100])  # ([79, 179, 279], [-100, 0, 100])\n",
+    "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n",
+    "ax.set_yticks([10, 30, 50, 70], [2, 3, 4, 5])\n",
+    "ax.set_xticks([39, 89, 139], [-100, 0, 100])  # ([79, 179, 279], [-100, 0, 100])\n",
     "ax.set_xlabel(f\"$\\phi$ [deg]\")\n",
     "ax.set_ylabel(f\"$\\eta$\")\n",
     "\n",