tracking-parametrisation-tuner/parameterisations/notebooks/magnet_kink_position.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import awkward as ak\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import mplhep\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "input_tree = uproot.open({\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_def_selected.root\":\n",
    "    \"Selected\"\n",
    "})\n",
    "array = input_tree.arrays()\n",
    "\n",
    "array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\n",
    "    \"ideal_state_770_tx\"]\n",
    "array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
    "array[\"x_EndT_abs\"] = abs(array[\"ideal_state_9410_x\"])\n",
    "\n",
    "array[\"z_mag_xEndT\"] = (\n",
    "    array[\"ideal_state_770_x\"] - array[\"ideal_state_9410_x\"] -\n",
    "    array[\"ideal_state_770_tx\"] * array[\"ideal_state_770_z\"] +\n",
    "    array[\"ideal_state_9410_tx\"] *\n",
    "    array[\"ideal_state_9410_z\"]) / array[\"dSlope_xEndT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4QRgAAAD8j1WFU/12/ulf/zL4r+ALtk8QBgAAAHtsXwM2+5+xDQ+2PYBDUalUIpfLxfHxcRwdHcXp6WkUCoVoNpt72fe+tAkAAAAwLzPC7qnZbEahUIhutxvZbDZqtVqk0+lot9tRKpUil8sNfp9KpXa+731pEwAAgNUzS4tDJwi7h2azGblcLiIizs/P4/LycvC3dDod+Xw+zs7OotlsxtnZWbRarZUFPevoe1/aJNk8MQMAwHbt61LMXeO9zXYIwpbU7XajUChExNdAZzjgGVar1eL09DQ6nU4UCoVoNBo72fe+tAkAAADDVhkoCacOnz3CltRf6hcRUSqVph7Xn/UU8XV2VLVa3cm+96VNAAAAgGWZEbaETqczssH7s2fPZh7//PnzqNfrEfE1EDo/P9+pvvelzU15/fp1fP78OR4+fBgvX77c2jg2ZZv3d1193/Upzq//7x/jv//93+Ovvv02vvu/v11Zv/PYZt/bsq37nMTHWX39NhF9J/E+b5PHeXN9q6/fHny/2+xbff12b/teZBnmpu7zpDGN972qWWN33f9/+te/rKSfZezre2dB2BLK5fLg52w2e+eeVv3ZThFflwvW6/WR3227731pc1Nev34dHz9+jMePH+/V/8zL2ub93Vbfvzb+Mf7r5s/xzfH/2fyLgi32vS3bus9JfJzV128T0XcS7/M2eZw317f6+u3B97vNvtXXbxPR96x+172vWRJrbF/fOwvCljC8dC+Tycx1m3Q6HZ1OJyIi3r59u3TIs46+96VNAAAA2Ae+UGB3CcIW1G63R67/8MMPc90uk8kMQp7+EsBd6Htf2mS32VASAACAfSAIW9DwvlcRX2cxzWP8uHa7PfdMqXX2vS9tkgw+NQEAAGCdfGvkgt6/fz9y/a69r/q+++67kesfPnzYib73pU0AAACA+xKELai/dK9v2dlOV1dXO9H3vrQJAAAAcF+WRi5oPORZVrfb3Ym+96VN/teq9uPqt9P/ut1/+te/rHVp4qS2N9U3AAAARAjCFrZsODO+PPDTp0870fe+tJkkqwiFhEoAAABw21Gv1+ttexD75OjoaOT6vP98zWYzcrnc4Ho2m41Go7H1vvelzUX8zd/8Tfznf/5nREQ8eLD46t8vX74Mfn7w4EH83d/9Xfzt3/7twu0s6vr6+mvfR0fxTerh2vvr+6/u54heb+P9brNv99l9PtS+3Wf3+VD7dp/d50PtO2n9brNv99l9PtS++/0+ePAgHj16tPb+/vKXv8Q///M/R8Tt986L6N/2r//6r+M//uM/VjfAOZgRtqBUKrWSJXvzbiC/7r73pc1F/Pd///fg5+H/MZfx5cuX+POf/xx//vOf79XOQnq9+K+bDfa37X632bf7nIy+3edk9O0+J6Nv9zkZfbvPh9/vNvt2n5PRdwLv85cvX+Ljx48b73e4/2UMv3/fFEHYgk5OTlYS8pycnOxE3/vS5iK+/fbb+Ld/+7fo/U8qfl+bmhEGAAAAu2x4Rth9fPnyJY6OjuLbb79dwagWIwhb0LKzlMaDoWVnhK26731pcxH/8i//stTtAAAAgMN2/+kyCfP999+PXJ935tP4xu+np6c70fe+tAkAAABwX4KwBZ2dnY1c73Q6c93u6upq5Ho2m92JvvelTQAAAID7EoQtaHy207whz/CsqFQqFel0eif63pc2AQAAAO5LELagTCYzsnfV+/fv57rdhw8fBj+PB0Xb7Htf2gQAAAC4L0HYEp49ezb4ud1uz3Wb4eNKpdJO9b0vbQIAAADcx1Gv1+ttexD7pt1uj+yDddc/YbPZjFwuFxER6XT61l5Y2+57X9oEAAAAuA8zwpaQyWRGNnKv1+szj6/VaoOf75rpVK1Wo1QqTd1Xax1970ubAAAAAPdhRtiSOp1OnJ6eRsTX0KfVak08rtvtxvHxcUR8/RbERqMxtc1cLhfNZnNw/ebmZmSvrXX2vS9tAgAAACzLjLAlpdPpwSymdrsdlUpl4nFPnz6NiK/fgjg862mS4RAsIuLdu3cb63tf2gQAAABYliDsHvL5fDQajUilUlEqlaJQKES73Y5utxvNZjPOzs6i3W5HJpOJn3/+eeLsrmHjf0+n0xvre5/aBAAAAFiGIOyestls3NzcRLlcjk6nE0+fPo3j4+MoFApxcnIStVotWq3WXAFPrVaLdDodqVQqisXiyB5b6+5739rcpE6nM9jIf9nb3rVH2l0qlUrkcrk4Pj6Oo6OjOD09jUKhcGsW4bbbZDmL1Fi73Y6Li4s4PT2No6OjwWNXKpWi2+0uPQY1drgWqa9OpzNSX8fHx3F2dhYXFxdT966ch/o6bPd5nhzWr7tlnjPV2OFaVX3dp031dbhWXV/tdjvq9XpUKpW5H0v1ddjWcQ5blBqboAcJdXNz04uIuS7n5+cLt53P5we3v7y8XGqMjUajl0qlehHRy2azvUaj0bu6uurVarVeOp0e/P7m5marbTLZKmtsvKamXRatNTW2v1Z9DiuXy3e2Uy6XFxqj+tpv63yeHFcsFgdt1Wq1uW+nxvbXOupr1W2qr/21qfPXzc1Nr1wu9zKZTC8ieqlUqlcsFnuNRuPOx1B97bdV1Vj/8VrmclftqrHpBGEk1jxv+vqXq6urudq8ubkZeTG/bDjR6309ydx1kus/6abT6blONutok+lWVWM3NzeDJ5ZVvqBTY/ttleewbDY7eAGfz+d7xWKxl8/nJ9bdvCGF+tp/63ienGT4cVVjybGO+lplm+prv637/HVzc9M7Pz8ftJHJZHqNRmPu26uv/beKGqvVanO3Meky6/lSjc0mCCOx5k3fs9nsXO2Vy+VeOp0evKEcviwahN3c3AzGl06npx53dXU19zjX0SazrarG+jWVyWR6tVqtd3V1NfjkZVLwOs8bSTW2/1ZVX/0amjbba9ILvbuor8Ow6ufJSYYf13nPX+O3U2P7aR31tao21df+W+f56/LycqR9r/OTaRU1Nul94yKXadTY3QRhJNLl5WUvIgZTl2dd5kmyW61Wr9Vq3Wp/2SfI4ZPiXbeddwnmOtpkulXV2HA701xdXQ0+felfUqnUzPGpsf22qvrqv1i561Ps4U+9I2LkfDeJ+tp/q36enKY/83D4DcU8QZga22/rqK9Vtqm+9ts6z1/DH0Cm0+mlZpOpr/23ihrrvwZLp9O9y8vL3tXVVe/m5ubOy/Dyw2nU2N0EYSRSOp2emWTfV6vVWjoIG07RI+LOJ+jhKbXTwo91tMlsq6qx/izDu4w/xrPCDTW2/1ZVX/l8fq59v8b3wZh1G/V1GNb9PNnr/e8biVartVAQpsb23zrqa1Vtqq/9t67z1/Cb+1QqtdSHAOrrMKyixorF4sLLB4cf62nvL9XYfARhJE7/f8x1ptPjJ4tF+hqeeTHvdNLhvia9gVhHm0y3qhrrB6rzPkGOL2GbFlaosf22ynPYIlPWh/cLm9W3+tp/m3ye7M92XSQIU2P7bR31tco21dd+W9f5a3wJ27L7Iqqv/beqGkulUnfOsB83/Fp/2vsDNTYfQRiJk8lklv4UZ173CcKGbzdrOdyw4Teo+Xx+I20y3apqrFgsLvRNRuMzEac91mpsv23iHDbJcFAx64Wb+tp/m6ixTCbTy2Qyg+uLBGFqbL+to75W2ab62m/rqK/7bnkyTH3tv1XV2CJfrjDc911hlBqbz4OABGm329Fut6Pb7cbx8XGcnp7GxcVF1Ov1bQ8tIr6Ob9gPP/ww1+0ymczg5/H7so42mW6VNfb8+fMol8tzHz/8mEVEnJ6eThzfMDW2X7Z1Dut2u9HtdiMiIpvN3qq14fENU1/7ZxM1ViqVot1uR61WW2p8w9TYfllHfa2yTfW139ZRX51OJy4uLgbXM5lMnJ+fLz2+Yepr/6yyxrLZ7ELHd7vdweNdKBSmjm+YGptOEEailEqlkeudTieq1WoUCoU4OjqKQqFw63/2TWo2myPX0+n0XLcbP274PqyjTaZbZY1lMplIpVJz990PKvomPdZqbL9t6xz27t27iPj6mM0KL9TX/lt3jbXb7ahUKnF5eTn3YzlMje23ddTXKttUX/ttHfU1Hjj8+OOPS49Pfe2/bb6XrFarg5+fPXs28Rg1Nj9BGInR6XRu/Y88rl6vx9nZ2cgnP5v0/v37kevzhiDffffdyPUPHz6stU0m23aNdTqdkeuTPmlSY/trW/XV7Xbj4uIiMplMNBqNmY+v+tpvm6ixp0+fRj6fX3pGhRrbX+uor1W3qb7217rqa/jNeyqVinw+v/QY1dd+2/br/Ldv30bE7A/K1dj8vtn2AGBT0ul0XF5eRrfbjaurq2g2m7eCg75qtRofPnyIVqu10TGOj2fZxP3q6mqtbTLZtmts+Alm2ptMNba/tlFfnU4ncrlcpFKp+Omnn+588aO+9tu6a6w/s+LNmzdLj1GN7a911Neq21Rf+2sd9XV5eTlyvf8BY71ej7dv30a73Y5OpxOpVCrS6XQ8f/48zs/Ppz5Xqq/9ts3X+cPLIp8/fz71ODU2P0EYiTIeDnS73ahWq/Hq1atby8ra7XbkcrloNBobG9+0k+mihu/LOtpkum3W2PALtvGp231qbL9tsr7q9frIkpDj4+Mol8tRLBan3kZ97b911Vi9Xo96vX7nrMK7qLH9to76WmWb6mu/rbq+hpeiRUScnJzE2dnZrSVe/ZCi3W5HqVSKWq02ceaY+tp/23qd39+iIiJmzkpUY/OzNJJES6VSUSwW4+bmJmq12q0X581mMyqVysbGs+wJYnzcnz59WmubzG9TNTY8fb9cLk/9tEaNHZZV11e3241KpRKnp6cTN2ItlUpTN2jt334Z6mt3raLGut1uFAqFOD8/X3hz4EltLUON7aZ1PEfep031dVjuUwudTufWY/fu3bu4uLiIm5ub6PV60ev14urq6lY4UigUJm4Orr4Oz6Ze5/c/7M5kMjNnZKmx+QnC4H/k8/n4+eefb30b2qtXr7Y0ouWtI3HftRR/H62zxvrfLplOp2fO2FkVNbZ7VlFfzWYzrq6uIpvNTg0s6vX62j8gUF+7adkae/r06WBJya5QY7tnHc+R23ptp752z6K1MD4LJpVKxc8//3xr6WP/3DZ+fnvx4sXaHjP1tZvWdb6Zd1nkKiWhxgRhMCSVSkWr1Ro5gXW73Ts3Rlxl/6tuZx1tsrx11Fi73Y5qtRqpVOrO6ddq7LDdt77y+fzgBX2j0YherxeXl5e3HptSqTTxBY36OnyL1lilUol2uz3z20YX7X/V7aix3bGO58hF2lRfh22RWhgPwmbt/dX/+/DMsG63eysAUV+Hbx3nsHmXRfb7X4Uk1JggDCYY38h3U/uEnZycrLyddbTJ/a2yxl68eBERET/99NOdG1iqsWRYZX2dn59Hq9W69QJmfO+UCPWVJPPUWH+/nHK5fOsT8mWpsWRYx+uwedpUX8kwTy2Mb+z9ww8/3Nnu+P6s47On1VdyrPIc1p9tmE6nvc5fIUEYTJDJZEaWBa1qk8C7LJuUj8/MWEWKP6tN7m9VNXZxcTGYbTHPG001lgyrPoel0+n46aefRn43/nXaEeorSeapsUKhEJlMZqXLtdVYMqzjddg8baqvZJinFpb590+n07deiw1vrK++kmNV57DhZZF3zQaLUGOLEITBFLlcbuN9fv/99yPX511LPb754Onp6VrbZDXuW2PVajWq1WpcXl7O9eQYocaSZNXnsEwmM1Jnk17Uqa9kmVVjlUolOp1OnJycRKFQuPMy/Li+evVq5G/Dm06rseRYx+uwu9pUX8lxVy0s++89/ngPP1eqr2RZxTlseFnkPPuDqbH5fbPtAcCuGp56uqmpnGdnZyPXO53OXLN8xqdvD38CsY42WY371Fiz2YyLi4u4vLy89W1Fs6ix5FjHOez58+eDUGLSCyH1lSyzauzXX3+NiFhqX5R2uz0yiyKdTg9CWDWWHOs4h93VpvpKjrtqYXz2yrxv/sff7A+HAeorWVZxDuvvr5lKpeZ6XNXY/MwIgymGT16bmso561OkWYafnFOp1MjY19Emq7FsjbXb7cjlclEulxcKwSLUWJKs4xw2/MJnUpvqK1k8T05vk/tbR33d1ab6So511cJ4W8MBiPpKlvuew4Y32Z/39b4am58gDKb48OHD4OdNLZPMZDIjJ8pJe/BMMjzW8ZPVOtpkNZapsU6nE0+fPo1isbjUvjtqLDnWfQ6b9Jipr2SZVWPlcjl6vd7cl+EXyLVabeRv5XJ58Dc1lhzrOIfd1ab6So55amHY+OyWeQ2f29RXstz3HLbossgINbYIQRhMMfyEt8mpnM+ePRv8PLw0ZJbh48a/sWZdbXJ/i9ZYp9OJs7OzOD8/H3ljeNdtxr+1SI0lwzrOYcOfAk57Uae+ksPzpBpbp3XU1zxtqq9kmKcWhn8/7zLv8SWU44Ga+kqO+57DFl0W2afG5tQDJkqn072I6BWLxYVve3V11YuIweXy8nLu27ZarZHb3qXRaAyOTafTG2uT+1ukxm5ubnrpdLp3fn6+UB+ZTKbXarVGfqfGkuE+57BpyuVyLyJ6qVRq6jHqKzlWWWP9tiKiV6vVZh6rxpJhHeewedpUX8kwTy0MPw4R0bu5ubmz3fPz88Hx2Wz21t/VV3Lc5xx2c3MzeIwWfe2vxuYjCIMJarXa4M3ePE9648ZPFosEYb1er5fNZud+QzD8hDurn3W0yfIWqbF+CJbNZntXV1dzXRqNRi+TyfQymczENtXYYbvvOWyaVCo11+Orvg7fqmtskSCs11Njh24d57BF2lRfh22RWhg+N80TaAwfP/5BZJ/6Onz3PYddXl4OHqNGo7Hw7dXY3QRhJEKj0Ri8gctms1OfmHq9r7O5+sfOOm6W/smvfymXywvdfnhG2bQgo9cb/bRg0qdO626T/7XOGstkMiP1tMhl2pOPGtsv66qvcrncy2QyvWKxeOcLtWKxOPcbAfW1fzb9PDlu0SBMje2XddTXOmtWfe2XddbC+IfbV1dXM8fRP27WLB71tX82/RzZD51mzcCfRY3dTRBGIgyn0rOeoPonuXQ6PfOJbpb+7J3hvmadLKYZDtOmBWn9gGTeTxvW0SZfravG7hOCRcw+xaux/bGO+hp+odK/TAu58vn8zMd0EvW1Xzb5PDnJokFYr6fG9sk66mvdNau+9se6a2F4dk46nZ74uAy//p/ndb/62i+bfi/Z7yOfzy89ZjU2myCMRBhf49+/pFKpXj6f752fnw/+p51nZsS4m5ubXj6fH5kyOumSzWZ7+Xx+7k8Hhj996N/u5uZmsOyt/2S7yHjX0SbrqbF++LDsZZ49BdTYfljXOWw8tB9us1gs9rLZ7OD6Mi/o1Nf+WPfz5F2WCcL641Zju28d9bWJmlVf+2HTtZBKpXrlcrnXarV6rVarVy6XB39bZD8n9bU/Nvkced9lkePjVmOTCcJIjKurq975+XkvnU6PPJGl0+lePp/v1Wq1nf0ftr98aXjc2Wx2oTcLm2gz6dTY+ttMsnXU183NTa9YLN56nPptXl5erqRm1dd+cA5bf5tJto762lTNqq/dt6lauLy8HHxI1O+jv8XAsjOA1Nd+2FSNDU+sWBU1dttRr9frBQAAAAAcuAfbHgAAAAAAbIIgDAAAAIBEEIQBAAAAkAiCMAAAAAASQRAGAAAAQCIIwgAAAABIBEEYAAAAAIkgCAMAAAAgEQRhAAAAACSCIAwAAACARBCEAQAAAJAIgjAAAAAAEkEQBgAAAEAiCMIAAAAASARBGAAAAJA43W43jo6O7ry02+1tD3VnVSqVO//9Tk9Ptz3MEUe9Xq+37UEAAAAAbFK3243j4+PB9UwmE2/evIl0Oj1yXCqV2vDI9ku32x38/OnTp2g2m3FxcTH4XTqdjqurqy2MbDJBGAAAAJA440FYrVaLfD6/xREdjkKhEPV6PSJ2LwizNBIAAABIPDO/Vmd8Vt0uEYQBAAAAiXdycrLtIbABgjAAAAAAEkEQBgAAAEAiCMIAAAAASARBGAAAAMCCut1uVCqVODs7G3xDYl+9Xo9cLhfHx8dxdHQUZ2dnUa1Wp7ZTKpXi7Oxs5PjxNnd9HPvim20PAAAAAIgolUpRqVSWum0mk4lWq7XiETGu3W5Hs9mMt2/fRrvdvvX3TqcThULh1t/a7XZcXFxEq9WKy8vLwe8rlUqUSqWJ/RQKhSgWi1Eul3d2HPtIEAYAAAA7oNvtRkREOp2Oi4uLSKfTE497//79rcDsUEKKXdbpdOLVq1cRERPDp2q1GhcXF5HJZKJcLkc6nR7cpv/YVqvVKBQKkc1mI5fLxYcPH+L8/DzOzs7i5OTk1mNbqVRu1cKujGNfHfV6vd62BwEAAABJd3FxEe/evYubm5uZx52dnY0EIOfn5yOze5hPt9uN4+PjwfVWqxWZTGau247PoEqn0/Hp06d48+ZN5PP5kWM7nU6cnp4Orvf7ODk5iVqtFqlUauT4ZrMZuVxucH3W47sr4xg3PLsxnU7H1dXVXLfbBHuEAQAAwI748ccfZ/69UqmMhGCpVEoItgWTArObm5tb4VPE1yAom80Orrfb7eh2u9FoNG6FTxER2Wx2ZOZVs9nc+XHsE0EYAAAA7IBPnz7NnJHU6XRu7eNUq9XWPSwmODk5Gbk+KXgaNjyzKiIm7sc1bLgOOp3Ozo9jnwjCAAAAYAeUy+WRGTvjCoXCyPXz8/OZx7M7xmdcjQdY48b34lpVCLUr49gmQRgAAADsgFkbkVer1VtLIhfZIL9SqUQul4ujo6PB5ezsbKHxVSqVkdsfHx/HxcXFQm0k1V2B0136m9wfyji2SRAGAAAAO6zT6dwKnCZtbj5LsViMRqMxsnRukdk97XZ7ZBldKpWKn3/+2f5ka/Ldd99tewgRsTvjWCVBGAAAAOyw8RAsn88vvSRyOPxaZHbP+LLMZ8+eLRTEwa4QhAEAAMCOqlarI9/Wl0ql4s2bN0u11e12o91ujwRY88wKK5VK8enTp5Glm+PBGOwLQRgAAADsoG63e+tb/d68ebP0TKx+oPbs2bPB7+4KwtrtdlQqlXjz5s3IsTbpZ18JwgAAAGAHFQqFkeWL+Xx+ZI+vRb19+zby+Xycnp4OfnfX8shCoRDn5+cjvxOCsc8EYQAAALBj6vX6ypZE9jWbzcjlciNLHGfNCOvvTXZ5eRlv374d/D6Xy83VX3+T/1wuF4VCYRDs5XK5OD4+Hrl/08Z6dnY2uMw6HuYlCAMAAIAd0u1248WLFyO/u8+SyIivSxy73W5ks9mRIOzq6mri8c1mM6rVatRqtcH1vnlmpVUqlTg9PY3T09NoNBpRq9WiXC5HoVCIZrM5GMsk/fAsl8tFq9WKVqsV6XQ6crlcVCqVwX2BZQjCAAAAYIe8ePFi4SWRlUolqtXq1L+/ffs20un04NI3aUZYt9uNQqEQxWIxMplMdDqdwXhSqdTI7ScpFApRKpWiVqtFsVgc/D6dTsenT58iYvryykKhENVqNc7Pz0du+/z584j4unH/2dnZrb3TYF6CMAAAANgRzWYz6vX64Pq8SyIbjUacnJzMbLcfPt31rZEvXryIk5OTKJfLEREj4xneaH+Si4uLqNfrcX5+PjG86wdqk5ZXVqvVQV/9vvsymczg56urq7i8vJw5jl3XDwS3bVfGsUmCMAAAANgRhUJh5Po8SyK73W40m82pM7W63W602+2Rtvttjgch9Xo96vX6YElkxNeQrW/W/mD1ej2q1WqkUqmpQVU/eJs0I6zfZz6fv3WfhwO7u77pcheN/zvftbTz119/PehxbJMgDAAAAHbAxcXFSDCRzWbn2o+rv5/YtCCsv7/XcPjUP3a4v/6SyHK5PDIDa979wfrj+PHHH2eOI5VKjbQ//vcffvjh1t+Gw7jvv/9+6hg2ZTyMuytQGv/7tL3Zph0/LfzblXHsE0EYAAAAbFl/c/ph/Y3lxy/9mVelUilOT08HywmnzRx7+/btreBp0j5hhUIhMpnMyN5cwyHYpPCqr1KpDEKT8/Pzicf0Z3xNC7Kmjb/b7UalUomIiGKxeK8vDViVV69ejVx/9+7d1BCq2+3emiH37t27meHWu3fvRn43bYbdroxjr/QAAACArcpkMr2IWPqSSqWmtp1KpXrlcnnkd8VicXDbRqPRK5fLvYjoXV1djRx3fn4+OG68jfE+IqKXz+cn/v3m5ubOdmq1Wi8ieplMZuR26XS6FxG9YrE4tf9lDI8pInqtVmvm8a1Wq5fNZgf3ddIlk8n0arXa4Pi7HtdMJtO7vLzs9Xq9XqPRGNzXaY9xNpvt/cM//MNOjGO8VoYN11c6nV7Fw7Uy39wvRgMAAADuq9VqraXddrsd3W731p5cp6eng58bjUZUKpUol8u3llcOzwib9k2P/T4ipu8hNjxzaVo7+Xw+Wq1WPH36NM7OzuLk5CQ+ffoUmUwmGo3Gnd9WuW79cSxy/CKPazabvXOpYt/f//3f78Q49pEgDAAAAA7U27dvJ+7JNfwNk5VK5daSyIivS+n6y+am7esVMbpv1KRlj51OZ+4llq9evYrvv/9+ocAJFmGPMAAAADhQ9Xp94gys8dlVw98S2Te8P9S0WVwRd2+gXiqV4vnz5yPtdLvdwd5mfYVCIer1+sygDO5LEAYAAAAHqN1uR6fTmfgtjMNB2OXl5cRlh8Ph2LQlj+NtvX37dvBzt9uNXC4X5XI53r9/HxFfZ4N1u9148eLFrXCtP2usUqnE2dnZrS8PWLdPnz5ttD+2QxAGAAAAB6hUKk39W/+bF7PZ7MRveex/Q+U8stnsoL1KpRIXFxdRKBTi7OxsELL1Z43V6/V4+vRplMvlW9/+WC6XBz+32+24uLiIo6OjuLi4mGsc93XXzDbmN+2bK3eBIAwAAAAOSLPZjLOzs0GQVSqVolAo3Aq2MpnMrSWR1Wo1crncrRlg/XBr0iytVCoVP/3002BJ47t37+Lk5CRardZgtlh/aWQ6nY5arXZrBlq9Xo9arRZXV1dRq9VGZotVq9WRzf3XpVQqRbPZjG63O3JhtvF/r2azufHZfIs46vV6vW0PAgAAAEimSqUSb9++vfXNhp1OJ0ql0mAvsfPz87i8vFxZv91uN46Pj+88rtVq2bdsikqlMnPmYcTX8HOXvoVSEAYAAABsRb1ej0KhMDNsKpVKUalUdi5QYT8JwgAAAICtyOVy0Ww2465o4ujoKDKZzK1ZY7Aoe4QBAAAAW9HfMH/WXlz9v41/yyQsQxAGAAAAbEV/E/1Xr15NPaZUKkU6nY4ff/xxU8PigAnCAAAAgK3I5/NRLpejUqnExcVFtNvtiIjBtw/mcrn49OlTtFqtwewxuA97hAEAAABb1e12o1qtxtXVVXz69CnS6XScnp5GNpuNdDq97eFxQARhAAAAACSCpZEAAAAAJIIgDAAAAIBEEIQBAAAAkAiCMAAAAAASQRAGAAAAQCIIwgAAAABIBEEYAAAAAIkgCAMAAAAgEQRhAAAAACSCIAwAAACARBCEAQAAAJAIgjAAAAAAEkEQBgAAAEAiCMIAAAAASARBGAAAAACJIAgDAAAAIBEEYQAAAAAkgiAMAAAAgEQQhAEAAACQCIIwAAAAABJBEAYAAABAIgjCAAAAAEgEQRgAAAAAiSAIAwAAACARBGEAAAAAJIIgDAAAAIBEEIQBAAAAkAiCMAAAAAASQRAGAAAAQCL8f9VGx7hum6xuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.hist(array[\"z_mag_xEndT\"],\n",
    "         bins=100,\n",
    "         range=[5100, 5700],\n",
    "         color=\"#2A9D8F\",\n",
    "         density=True)\n",
    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Juha5XxzsZfWEFcCiW8SWA4AVBGEAAGChrfoL7eevjQ/CWEGy+ugXN7lVDskBAM4hCAMAAFhwrCBZffSLm9yqh+QAAGfQIwwAAMABP3047wqwTOgXBwCAOwjCACwMGm4CWBZWQq5vlQnDYF23X5wV9IsDAGB6bI0EsDDY4gBgGTz+WHr5F9bGfufn0rN/R2gBa+gXd+qnD6UXY/OuAgCwqlgRBgAAMIGf1KU//pu1sR/9WXq77mw98JZl7xfHakoAwLwRhAGYCdsZAXjNL9+fbPyvJhwPrKpJV1O2Pna0HACAR7E1EsBM2M4IwGtO/uTseGBVTbOa8rtfcrYmAID3EIQBAABMIPg5Z8fPYs13+s+HcWOAeZhmNaUbQVinc/qPvVHWfJKPnx1b8XUHMC8EYQAAABN49hnp3d9aH//VZ5yr5SyfjxW4WFyLupqS1e3zwdcdwLzQIwwAAGACL0Slz3/G2tgnPyu9GHW2HmBZLPJqSgCAd7AiDACAJcbWEvddeUL60ddOz2w3zltfl/xPjB7DdkZ4xSKvpgQAeAdBGICVs6jBwKLWheXG1pL5eP7a+CDs7eTpuHHYzgiveCEqvfpraw3zWU0JAHAKQRiAlbOowcCi1gXAGd+0EIIBXmL3akoAAKZBjzAAWFE/fTjvCgAAGGRllaTV1ZRYXLwGAbDIWBEGAEvIygvMb5WlS2u8mQAAL1rm3nOsplxsvAYBsOxYEQYAS+bxx9LLv7A29js/l1ofO1oOAGAB+XynQcSoCz0pMSlegwBYBQRhALBkflK31mhYkj76s/R23dl6AACA/Tod6dP26EtnzEl47MZrEACrgK2RgAdx9sLl9sv3Jxv/q/el737JmVqcwvcoAMDrFvEkO7wGOcVrEGC5EYRhqTWbTYVCoZFjdnd3tbu761JFy2ERX1jBupM/OTt+EfA9CgDA4uE1yClegwCj7e/va39/f+SYZrPpUjXnEYRhqbXbbR0dHY0c02q1XKoGcEfwc86OBwAAGIbXIACsaLVaY9+nzxNBGJba2tqaNjY2Ro7x+/0uVQO449lnpHd/a338V59xrhYAAOAdvAYBYIXf79fm5ubIMc1mU+1226WKBhGEYaltbGzo8PBw3mUArnohKr36a2vNap/8rPRi1PmaAK9Z851ujRk3BgBWCa9BAFhhpT1RKBSa26oxzhoJAEvmyhPSj75mbexbX5f8TzhaDuBJPp90aW30hUbKwPz99OG8K1gtvAYBsAoIwgBgCT1/bfyYt5PWxgEAsIyshFzfKhOG2Y3XIACWHUEYAKyob/ICFAuON6fA6nD75/nxx9LLv7A29js/l1ofO1oOzuA1CIBFRhAGAABsx0oNYHUs4s/zT+rW+lRJ0kd/lt6uO1sPAGB5EIQBAABbsVIDWB2L+vP8y/cnG/+rCccDAFYXQRgAALDVPFZqsLIMcMairrw6+ZOz4wEAq4sgDAAA2MrulRqLuC0L8IpFXXkV/Jyz48/i9wsArI7L8y4AALAaOh2p3Rk9Zs0n+Xzu1IP5sXOlxqTbsp79O8n/xGTHBxbFmk/65PXxY9y0qCuvnn1Geve31sd/9ZmLb7Matl9a40yIALAKCMIAALZod6TLr40e88nr0iWCsJVn50qNabZlffdLkx0fWBQ+3+L9jnR75ZVVL0SlV39t7ffDk5+VXowOv42wHQC8h62RAADAVs+OWHkxzKiVGou6LQvwCjt/nu105QnpR1+zNvatr18cXi1qDzQAgHMIwgAAK63TkT5tj750xmzpxGReiEqf/4y1saNWakiLuy0L8Ao7f57tZmWb4tvJ0eMI2+EkXoMAi4mtkQCAlcaWTfd1V2p8qzx+7KiVGtLibssCvMLOn+d5+OaYsIywHU7iNQiwmFgRBgAAbGfHSg1pcbdlAV5i18/zIiJsB7BsWGk4O1aEAQCAuRi3UkOyryE2AGdZ+XleRHaefRIA3MBKw9mxIgwAACwsuxpiA8Awi9wDDQDgDIIwAACw0FZ5WxaA+SJsBwDvIQgDAABLb1m3ZQGYP8J2APAWgjAAAAAAGIGwHQBWB0EYAAAAAAAAPIEgDAAAAAAAAJ5wed4FAAAAAACc0+lI7c7oMWs+yedzpx4AmCeCMCy1ZrOpUCg0cszu7q52d3ddqggAAABeteaTPnl9/Bi3tTvS5ddGj/nkdekSQRgAG+zv72t/f3/kmGaz6VI15xGEYam1220dHR2NHNNqtVyqBgAAAF7m8xEmAUCr1Rr7Pn2eCMKw1NbW1rSxsTFyjN/vd6kaAAAAAIvopw+lF2PzrgKz4DlcHn6/X5ubmyPHNJtNtdttlyoaRBCGpbaxsaHDw8N5lwGP448yAADWLOrWQSwmq6+xfvpw/JhvlaVLa9Lz12avC/bjOVwtVtoThUKhua0a46yRADCC1T/KVsYBAOB1Pt/pG9lRFxq2e4Ndr7Eefyy9/Atrx/zOz6XWx9bGwj08h3AbQRgAXIA/ygAAAPaz8zXWT+rSH//N2lwf/Vl6u25tLNzDcwi3EYQBwAW88ke505E+bY++dMacch0AAMAqO19j/fL9yY79qwnHw3k8h3AbPcIA4ALT/FH+7pecqcVJnFIdAAC4yc7XWCd/mmyuScfDeTyHcBtBGABcgD/Ki22ZT1JAs2gAmB2/S5eXna+xgp+bbK5Jx8N5PIdwG1sjAeAC/FGen1U/SQHNogHnLOvvBUyO36XLy87XWM8+M9lcX51wPJzHcwi3EYQBwAX4ozwfnKQAwEVWPSQHvMLO11gvRKXPf8baPE9+VnoxOtmx4TyeQ7iNIGwODMNQIpGwPHZnZ0eRSEQ+n0/BYFCxWEw7OzsyDGPqGvL5vBKJhILBoHw+nyKRiFKplKrV6kLNCcwTf5TnwysnKQAwGUJyYHXY+RrryhPSj75mba63vi75n7A2Fu7hOYTbCMJsZJqmfD7f2EskElE4HB47Xz6fVyQSUbFY7IVepmmqXq+rWCwqEokon89PVGO1WlUwGFQ2m5UklUolNRoN5XI51et1JRIJJRIJmaY51zmBRcAf5fngzEEAhiEkh1O6vcZGXeg1Zi+7X2M9f238PG8nrY3DfPAcwk00y7dRsVi0PLYbGl0kkUioWq0qEAgoHo8rHA7LMAzV6/WBlWDZbFbhcFjJZHLsMavVam8lWjqdVqFQ6N3WnSMWi6larSoWi6lWqykQCLg+J7BInr92us1mFP4o24uTFAAYxitn8oX7fD7OjDwPbr/G+iav1ZYezyHsQhBmo729PUvjusHWRbLZrKrVqnK5nDKZzLnb8/n8QJCWSqXU6XRGHtM0TaVSKUmnAVV/YNWvVCopEonIMAylUilVKhVX5wSWEX+U7cVJCgAMQ0gOLAY3z9bJaywATiAIs0mxWJRpmspkMmP7f21tbV14m2EYyufzqlQqisfjQ8dkMhk1Go2BFWj1el3R6MWb51OpVG9r4qjVaN1VXOVyWdVqVcViUel02rU5AeDZZ6R3f2t9PCcpALyBkBxYDKygc1+nI7VHr3vQmo+zpAJWEYTZJJfLKRwOK5fLzTRPNptVLpe7MATrP15/EFatVi8MwgzDGGhYf+PGjZFz37x5U+VyuVfPsNDKiTkBrL6fPpRejI0e80JUevXX1noBcZICwDsIyQF4VbsjXX5t9JhPXiegBKyiWb4NyuWyDMMY2/fLiu6qsnECgcDA9spRfbf6w7l4PD62R1d/vzHTNHsBltNzAlhuP304fsy3yuPHcZICAMNwJl8AAGAHgjAb7O3tKRAIjF0VZcUk/bOOj49710dtt+xfOTZq+2S//pDt7t27rswJYHk9/lh6+RfWxn7n51Lr49FjOHMQgLMIyQEAgB0IwmZUr9dVr9dlmqaCwaAikYh2dnYcX/FkmmavP1c8Hr8wjKrXB88dfv36dUvz98939rE4MSeA5faTurWtjJL00Z+lt+vjx41DA13AewjJAQDArAjCZnR2O6RhGCoWi0qlUvL5fEqlUueCIzvcu3dP0ukqq1KpdOG4/j5e3fFWnB3X/xicmBPAcvvl+5ON/9WE4zEbK9tWgVVBSA4vsfP3O38rrONrBSw3grAZnG0YP0y5XFYsFtPOzo5txzVNUzs7O4pGo6pUKiP7cz148GDg43G9vLqeeuqpgY8PDg4cnRPAcjv5k7PjcTG7erMBABaLnb/f+VthHV8rYPURhM0gHA6rUCgol8spnU6PXBlVLBYVi405VZoFhmEoFospEAjo/v37Y1djGYZxrmYrzo5rNBqOzglguQU/5+x4DGd3bzYAwGKw8/c7fyus42sFeANB2IzS6bQymYwKhYIajYZOTk6Uy+WGrpKq1+tKJBJTH6tcLisSicgwjF5Psnw+P/I+Z0OraXX7kTk1J+A2/otnr2efmWz8Vyccj+Hm0ZsNAOA8O3+/87fCOr5WgDcQhNksEAgok8no5OREpVLpXCBWrVbHhlf9TNNUPp9XJBJRKpU6d3s2mx36+f77T+Ns3f1nqHRiTsBOLGl33wtR6fOfsTb2yc9KL1o72SzGoDcbAKwmO3+/87fCOr5WgDdcnncBqyyZTCoej2t7e3ugMfze3p4ymYylOarVqhqNhuLx+IU9ycrlsvL5vOU5p+HE6i075mw2mwqFQjPPs7u7q93d3ZnnwfxNuqT92b+T/E84WpInXHlC+tHXTgPGcd76Ol9zu9CbDQBWk52/3/lbYR1fK2C8/f197e/vzzxPs9m0oZrpEIQ5LBAIqFarKRaL9cIw0zRVrVYVj8fH3j+ZTCqZTA58rlgsKpvNDgRJ2WxW6XT63KqrQCBgS+DUP68Tc06r3W7r6Oho5nlardbMc2AxTLOk/btfcrYmr3j+2vgg7O3k6TjYg95sALCa7Pz9zt8K6/haAeO1Wi1b3oPPE0GYS+7cuTPQLL9SqVgKwoZJp9OKx+OKxWIDgVSxWDy3Kmx9fd2W0Gp9fd3ROae1tramjY2Nmefx+/0zz4HFMM2SdoIw93yTEMxWzz4jvftb6+PpzQYAy8HO3+/8rbCOrxUwnt/v1+bm5szzNJtNtdttGyqaHEGYS6LRqOLxeG9r46wN58PhsO7fvz8Qrj148ODcuGlXXZ0Nus6uCLN7zmltbGzo8PBw5nmwOljSDi95ISq9+mtrqyDpzQYAy8PO3+/8rbCOrxUwnl1thUKh0NxWltEs30WznDFymGg0OrBtcli4trW1NfCx1ZVcZxvZRyIRR+cE7MKSdnhJtzebFfRmA4DlYefvd/5WWMfXCquCk4KNRhDmonA43Ltux7ZASbp582bv+rBAqn/FmGR9JVqj0Rj4uH8bpxNzAnZ5dsIl6ixpx7Kz0nON3mwAsHzs/P3O3wrr+Fph0VkJub5VJgwbhSDMRf1BmB3bAqXTVWGj5jy7estqaNUfqgUCgYHanZgTsMsLUenzn7E2liXt8Ap6swHAarLz9zt/K6zja4V5efyx9PIvrI39zs+l1seOlrO0CMJcdHBw0Ltu9zZJ6XxAJZ0GZf0B2bA+YsP013p2XifmBOzCknZgMaz5pE9eH31Z8827SgAAgOXxk7q1HnaS9NGfpbfrztazrAjCXNS/NdCubYH9q7EuCtdu3LjRu16vW/tJ6B+XzWZdmROwC0vaMSmWjtvP55MurY2++AjCAAAALPvl+5ON/9WE472CIMxF5XJZkpTJZGybsxsuBQKBgcb5/XZ2dnrXu2etHKV/TDgcHhraOTEn4CaWtHsHfRQAAACwCiY94/2k472CIMwl5XJZhmEoEAjo9u3bts27t7cnSbpz586FY6LR6EDw1A3kLlIqlXrXL1q55cScAGA3+iisBrZZAgAAOy3rP0AnPeP9pOO9giBsStVqVcFgUD6fT4lEYuT2QMMwdOvWLUnS/fv3RzbKz+fzisViymazQ88C2a87JpPJXLgarKtQKPSud8OzYUzTVLFYlHS6fTOdTrs6JwDYiT4Kq4FtlgAAuGdZQ6KuVd4N8OyEZ7z/6oTjvYIgbEqlUqkXVFWrVcVisYHtgl3d29bX19VoNAbO8niWaZrKZrOq1+vK5/MKBoMXrp5KpVLK5/PK5XLK5XJj6w2Hw71VWd35h9ne3pZ0utWyfxWXW3MCgJ3oowAAAPAXqxwSSau/G+CFqPT5z1gb++RnpRcvjh88jSBsSqlU6tznisWigsGgUqmUdnZ2FIvFlEgklE6nVavVFA6HR84ZCATOjekGYqlUStlsVolEQsFgUNJp8/1J+o0lk0lVKhUFAgFls1mlUinV63WZptkL7Or1uqLRqB49ejRy5ZqTcwKAXeijAAAAcGrVQyJp9XcDXHlC+tHXrI196+uS/wlHy1laBGFTisfjajQaSqfTCofDAwFPvV7X8fGxbt++rZOTE+VyOcsBUK1WUyaTUTQaPTenYRhKpVJ69OiRSqXS2GDtorq7NRmGoe3t7V7Qtr6+rlKppFqtNlFg5cScAGAH+igAAACcWvWQSPLGbgArZ75/O2ltnFddnncByywcDg/0ybJDIBCwtNVxVplMxtazVzo1JwDM4tlnpHd/a308fRQAAMCqmiYk+u6XnKnFKewGOPVNQrCRWBEGAFhZ9FEAAAA45YWQiN0AsIIVYQCAldXto/Ct8vix9FEAgOHWfNInr48fA2CxeSEkWtTdAJ2O1O6MHrPm4yzYbiEIAwCstOevjQ/C6KMAABfz+aRLvDkDlt48QqKfPpRejM0+j1UvRKVXf22tF5qbuwHaHenya6PHfPI6v2vdwtZIAFggy3qq6mVHHwUAALDq7G4ZYeV167fK7r6+5ayKsIIgDABcsogvFgAAAOANdoZEjz+WXv6Ftbm+83Op9bG1sXbgrIoYhyAMAFywyC8WAAAA4A12hUQ/qVvbfihJH/1Zertubaxb2A3gbfQIA7BU3O4zYJdpXiws2+mqAQAArOIkDIvLSkj0y/cnm/NX7/PaFouDIAzAwrC6dfDS2vItZebFAgAAmJdFDJ04CcNyO/mTs+MBJ7E1EsBCWPWtg7xYAAAA8+Lznf4jcdTFRyiFCQQ/5+x4wEmsCMNSazabCoVCI8fs7u5qd3fXpYowrVXfOsiLhcks4n+uAQAAcOrZZ6R3f2t9/Fefca4WLJ79/X3t7++PHNNsNl2q5jyCMCy1druto6OjkWNarZZL1WAWq751kBcLk2G7BAAAsIp/oLnvhaj06q+t/SP7yc9KL0adrwmLo9VqjX2fPk8EYVhqa2tr2tjYGDnG7/e7VA1msepbB3mxAAAA4Az+gea+K09IP/raaf/ecd76uuR/wvGSsED8fr82NzdHjmk2m2q32y5VNIggDEttY2NDh4eH8y4DNlj1rYNeeLHghf/GeuExAgAAWPH8tfGvbd9OLt9JrjA7K+2JQqHQ3FaN0SwfwEJ4dsKtgMu4ddDKi4BlfrHghUa8XniMAAAAdvnmkr6uxWojCAOwEF6ISp//jLWxq7x1kBcLAAAAAOAcgjAAC6G7ddCKZd06CAAAAACYL4IwAAtj1bcOAgAAAADmiyAMwFJh6yAAAAAAYFoEYQAAAAAAAPAEgjAAAAAAAAB4AkEYgKF++nDeFQAAAAAAYC+CMMCDrIRc3yoThgEAAADLgNftgHUEYYDHPP5YevkX1sZ+5+dS62NHywEAAAAwAv/EBuxFEAZ4zE/q0h//zdrYj/4svV13th4AAAAAw/FPbMB+BGGAx/zy/cnG/2rC8QAAAADswT+xAftdnncBANx18idnx8Mdaz7pk9fHjwEAAIB1i/Yaa5p/Yn/3S87UAqwKgjDAY4Kfc3Y83OHzSZcIugAAAGy1aK+x+Cc2YD+2RgIe8+wzk43/6oTjAQAAANiDf2ID9iMIAzzmhaj0+c9YG/vkZ6UXo87WAwAAAGA4/okN2I8gDPCYK09IP/qatbFvfV3yP+FoOQAAAAAuwD+xAfsRhAEe9Py18WPeTlobBwAAAMAZ/BMbsB9BGIChvkkIBgAAAMwd/8QG7EUQBgAAAADAHPz0oT3z8E9swDqCMACOs+sPPAAAALAsrLwG/laZ18qA2y7PuwBgFs1mU6FQaOSY3d1d7e7uulSR91j9A39pjeXaAAAA8IbHH0sv/8La2O/8XHr27+jvhdWxv7+v/f39kWOazaZL1ZxHEIal1m63dXR0NHJMq9VyqRrv4Q88AAAAcN5P6tIf/83a2I/+LL1dl777JWdrAtzSarXGvk+fJ4IwLLW1tTVtbGyMHOP3+12qxnv4Aw8AAACc98v3Jxv/q/d5nQxr1nzSJ6+PHzNPfr9fm5ubI8c0m021222XKhpEEIaltrGxocPDw3mX4Vn8gQcAAADOO/mTs+PhXT6fdGnOQdc4VtoThUKhua0ao1k+gKnxBx4AAAA4L/g5Z8cDmB4rwgBMbVH/wC/DcmEAAACsrmefkd79rfXxX33GuVoADGJFGICpPTvhH2y3/sD7fKdnqRx18RGEAQAAwCEvRKXPf8ba2Cc/K70YdbYeAH9BEAZgavyBBwAAAM678oT0o69ZG/vW1zmzOuAmgjAAU+MPPAAAADDc89fGj3k7aW0cAPsQhAGYCX/gAQAAgOl8k9fIgOsIwgA4jj/wAAAAAIBFwFkjAWAEzkAJAAAAAKuDIAwARvD5pEsEXQAAAACwEtgaCQAAAAAAAE8gCAMAAAAAAIAnEIQBAAAAAADAEwjCAABj/fThvCsAAAAAgNnRLB8APM5KyPWtsnRpTXr+mvP1YDKc2RQAAACwjiAMADzs8cfSy7+wNvY7P5ee/TvJ/4SjJdlu1YMizmwKAAAAWMfWSADwsJ/UpT/+m7WxH/1ZervubD1O8PlOV7ONuvgIkgAAAABPIAgDAA/75fuTjf/VhOMBAAAAjEdPXvcQhAGAh538ydnxAAAAgNdZ7clLGOYOgjAA8LDg55wdDwAAAHjZpD15Wx87Wg5Es3wA8LRnn5He/a318V99xrlaAAAA4E2rfHKjaXryfvdLztbkdawIAwAPeyEqff4z1sY++Vnpxaiz9QAAAMB7VvnkRvTkXTysCMNSazabCoVCI8fs7u5qd3fXpYqA5XLlCelHXzvtSTDOW1+X/E84XhIAAAAwd52O1O6MHrPmGx/QebEn7/7+vvb390eOaTabLlVzHkEYllq73dbR0dHIMa1Wy6VqgOX0/LXxQdjbydNxAAAAgBe0O9Ll10aP+eR16dKYIMyLPXlbrdbY9+nzRBCGpba2tqaNjY2RY/x+v0vVAKvrm4RgAAAAwMS82JPX7/drc3Nz5Jhms6l2u+1SRYMIwrDUNjY2dHh4OO8yAAAAAFutcvNwwEteiEqv/tpaw/xV6clrpT1RKBSa26oxmuUDAAAAwIJZ5ebhgJd0e/JaQU9edxCEAQAAAAAAOMRKr1168rqHIAwAAAAAAGCO6MnrHnqEAYBL6PUBAAAAAPNFEAYALvH5xp9eGQAAAOjHP1MBexGEAQAAAACwoPhnKmAveoQBAAAAAADAE1gRBgAAAACAB7DNEiAIAwAAAADAE9hmCbA1EgAAAAAAAB5BEAYAAAAAAABPYGskAAAAAACYCP3GsKwIwgAAAAAAwEToN4ZlxdZIAAAAAAAAeAJBGAAAAAAAADyBIAwAAAAAAACeQI8wwAWPP5berku/el86+ZMU/Jz07DPSC1HpyhPzrg4AAAAAAG8gCAMc9j8fSi/9XProz4Off/e30u3/Jf2Pr0v/97V5VLZ4OPMMAAAAAMBJBGGAg/7nQ+mFexff/tGfT2/3SXqeMIwzzwAAAAAAHEWPMMAhjz8+XQlmxXd+LrU+drIaAAAAAADAijAstWazqVAoNHLM7u6udnd3XaroL35SP78d8iIf/fm0h9h3v+RsTQAAAAAAOGl/f1/7+/sjxzSbTZeqOY8gDEut3W7r6Oho5JhWq+VSNYN++f5k43/1PkEYAAAAAGC5tVqtse/T54kgDEttbW1NGxsbI8f4/X6Xqhl08idnxwMAAAAAsGj8fr82NzdHjmk2m2q32y5VNIggDEttY2NDh4eH8y5jqODnnB0PAAAAAMCisdKeKBQKzW3VGM3yAYc8+8xk47864XgAAAAAADAZgjDAIS9EpSc/a23sk5+VXow6Ww8AAAAAAF5HEAY45MoT0v/4urWxb31d8j/hZDUAAAAAAIAgDHDQ/31N+p83Ll4Z9uRnT29//pq7dQEAAAAA4EU0ywcc9vw16at/J/2kLv3q/dOzQwY/d9oT7MUoK8EAAAAAAHALQRjggitPSN/90ukFAAAAAADMB1sjAQAAAAAA4AkEYQAAAAAAAPAEgrA5MAxDiUTC0th6va6dnR1FIhH5fD75fD5FIhFls1mZpjl1Dfl8XolEQsFgsDdnKpVStVpdqDkBAAAAAADsQhBmI9M0e2HVqEskElE4HB47VyqVUiwWU7FYlGEYvdsMw1A+n1cwGFSxWJyoxmq1qmAwqGw2K0kqlUpqNBrK5XKq1+tKJBJKJBIThWxOzAkAAAAAAGA3muXbaJJQqhsaDWOapmKx2ED4dZGdnR3VajUVCoWxY6vVam8lWjqdHrhPOBxWMplULBZTtVpVLBZTrVZTIBBwfU4AAAAAAAAnsCLMRnt7e5bGxePxkSvCUqmUDMNQNBrtra5qNBoqlUrKZDLnxheLRZXL5ZHH7K4wk04DqouCs1KpJOl01Vl3vJtzwh1rPumT10df1nzzrhIAAAAAAHuxIswmxWJRpmkqk8mM7f+1tbU1cp5qtapMJqNcLjdwW3eF1c7OjlKplOr1eu+2W7duKZlMXjhvKpXqbU0ctRqte4xyuaxqtapisah0Ou3anHCHzyddIugCAAAAgKn99KH0YmzeVWBSBGE2yeVyCofD58KraeaJx+Mj5wmHwyqVSopEIr3PmaaparWqeDx+brxhGAMN62/cuDGyhps3b/ZWmGWz2aGhlRNzAgAAAACwCH76cPyYb5WlS2vS89ecrwf2YWukDcrlsgzDGLkqyop6vS7DMHpbCUcZFrr1rxDr1z8uHo+P7dHVv7LMNM2h2y6dmBMAAAAAgHl7/LH08i+sjf3Oz6XWx46WA5sRhNlgb29PgUBg7Kqoce7evat0Om25mfzZ1V8ffvjh0HH9Tfyj0ailuft7mN29e9eVOQEAAAAAmLef1KU//pu1sR/9WXp7+JoULCiCsBnV63XV63WZpqlgMKhIJKKdnZ2pVjzdvHlzoq2VZwOo/q2S/fX1u379+sRzn30sTswJAAAAAMAi+OX7k43/1YTjMV8EYTM6ux3SMAwVi0WlUin5fL5zTe1HiUajlleDSeo1qu8adibK/j5eF40Z5uy4/sfgxJwAAAAAACyCkz85Ox7zRbP8GZxtGD9MuVxWuVxWOp1WoVCw/fj9hjXKf/DgwcDHVoO2p556auDjg4OD3oouJ+YEAAAAAGBWaz7pk9fHjxkl+LnJjjnpeMwXQdgMwuGwCoWCTNNUo9FQtVo9F051FYtFHRwcqFar2Xb8g4OD3vWLzsJ4tp5pV281Gg1H5wQAAAAAYFY+n3RpTNA1zrPPSO/+1vr4rz4z2/HgLoKwGZ0NoEzTVLFY1N7e3rmti/V6XYlEQpVKxZZj968wu+iMlRcFc5PqfyxOzAkAAAAAwCJ4ISq9+mtrDfOf/Kz0IhudlgpBmM0CgYAymYwymYzK5bJu3bo1EPhUq1Xl83llMpmZjmMYRq/HVi6Xu3BV1rRh09ntjsfHx47OOa1ms6lQKDTzPLu7u9rd3Z15HgAAAADAcrvyhPSjr0nfsnCOt7e+LvmfcLykhbG/v6/9/f2Z52k2mzZUMx2CMAclk0nF43Ftb28PNIbf29ubOQjrnl0yHA7PPJcVTqzesmPOdruto6OjmedptVozz+E0O/a6AwAAAADGe/7a+CDs7eTpOC9ptVq2vAefJ4IwhwUCAdVqNcVisV4YZpqmqtXq0Ob2VtTrdRWLRQUCgbHbLAOBgC2BU/9qLifmnNba2po2NjZmnsfv9888h9Ps2OsOAAAAALDHNz0Wgkmn7503NzdnnqfZbKrdbttQ0eQIwlxy584dxWKx3seVSmXqIOzWrVuSpPv3749tVL++vm5LaLW+vu7onNPa2NjQ4eHhzPMAAAAAAIDR7GorFAqF5raybG0uR/WgaDQ6EHxN23B+Z2dH9XpdpVJJ0ej4jnzTrro6G3SdXRFm95wAAAAAAABOIwhzUSKRmOn+xWJRxWJRhUJByWTS0n22trYGPra6kutsI/tIJOLonAAAAAAAAE4jCHNR/zbGSbcFVqtV7ezsqFAoKJ1OW75f/3ZMyfpKtEajMfBx/2o2J+YEAAAAAABwGkGYi/qDsEm2BdbrdSUSCeVyuYlCMOn86i2roVX/Kq9AIDBQuxNzAgAAAAAAOI0gzEUHBwe961a3SRqGoe3tbWUyGWUymYmPGY1GB0K3Bw8eWLpff61ngy8n5gQAAAAAAHAaQZiL+rcGWtkWaBiGYrGY0um0crmcpWMYhqF8Pj/wuRs3bvSu1+t1S/P0j8tms+dud2JOAAAAAAAAJxGEuahcLkuSpZVdpmkqkUjoxo0blkMwSUqlUudCtp2dnd71arU6do7+MeFweGho58ScAAAAAAAATiIIc0m5XJZhGAoEArp9+/bIsaZpKhaLKRwOK5vNyjCMsZdqtdprYh+NRgfmi0ajA8FTN5C7SKlU6l2/aOWWE3MCAAAAAAA46fK8C1hW1WpVqVRKpmkqHo8rl8udC6C6DMPQrVu3JEn3798f2yh/e3u7F3BFIpGJ6ioUChd+vjvX3t6eksnk0HGmaapYLEo63b45qjm/E3MCAAAAAAA4hRVhUyqVSr2zIHZXY/VvF+zq3ra+vq5Go3FhWNYVi8Us99wa5qKQKRwO91Zl1ev1c33Eura3tyWdntWxfxWXW3Ni+az5pE9eH31Z8827SgAAAAAACMKmlkqlzn2uWCwqGAwqlUppZ2dHsVhMiURC6XRatVpN4XB47JxOhGBdyWRSlUpFgUBA2Wy2dzzTNHuBXb1eVzQa1aNHj8auXHNqTiwXn0+6tDb64iMIAwAAAAAsALZGTikej6vRaCiXy6larer4+Li3Qqwb/Ny+fVvxeNxy+OPGaql4PK6TkxPl83ndvXtX29vbMk1TgUBAW1tbKpVKF25xdHNOAAAAAAAAuxGEzSAcDl/Yk2vRZTIZS2evnPecAAAAAAAAdmFrJAAAAAAAADyBIAwAAAAAAACeQBAGAAAAAAAATyAIAwAAAAAAgCcQhAEAAAAAAMATCMIAAAAAAADgCQRhAAAAAAAA8ASCMAAAAAAAAHjC5XkXAMyi2WwqFAqNHLO7u6vd3V2XKgIAAAAAwLv29/e1v78/ckyz2XSpmvMIwrDU2u22jo6ORo5ptVouVQMAAAAAgLe1Wq2x79PniSAMS21tbU0bGxsjx/j9fpeqAQAAAADA2/x+vzY3N0eOaTabarfbLlU0iCAMS21jY0OHh4fzLgMAAAAAAMhae6JQKDS3VWM0ywcAAPh/2rt/38bSNU/sj+r2jK/tmTJV7aTQldyjZFNT1YlTUZkNOyCrARv2RiIXC0cViGhsPChIQYULk53NYoMqMtjFbmSy/oIqcdMBDLGTblTils7W3Os7vnNv0UENeUmKkkiJPw51Ph+AAFl1eM77inx5xK+e9z0AAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkwlebbgAAAABAljzaifjj39y+DdtHEAYAAAAwZmcn4leCrgdJEAYAAACwIqrLskUQBgAAALAiqsuyxWL5AAAAAOSCijAAAABYMtPhIJsEYQAAALBkpsNBNpkaCQAAAEAuCMIAAAAAyAVTIwEA2ArW2wEA7ksQBgDAVrDeDgBwX6ZGAgAAAJALKsLYah8/foxnz57duM3Lly/j5cuXa2oRAAAA5Nfr16/j9evXN27z8ePHNbXmKkEYW+3z58/x888/37jNp0+f1tQaAAAAyLdPnz7d+j19kwRhbLVHjx7F06dPb9zm8ePHa2oNAAAA5Nvjx4/jm2++uXGbjx8/xufPn9fUokmCMLba06dP46efftp0MwAAAICYb3miZ8+ebaxqzGL5AAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC58tekGwH18/Pgxnj17duM2L1++jJcvX66pRQAAAJBfr1+/jtevX9+4zcePH9fUmqsEYWy1z58/x88//3zjNp8+fVpTawAAACDfPn36dOv39E0ShLHVHj16FE+fPr1xm8ePH6+pNQAAAJBvjx8/jm+++ebGbT5+/BifP39eU4smCcLYak+fPo2ffvpp080AAAAAYr7liZ49e7axqjGL5QMAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFi+UD5NyjnYg//s3t2wAAAGw7QRhAzu3sRPxK0AUAAOSAqZEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAgH/yb//TplsAAMAqCcIAgFyYJ+T6521hGADAQyYIAwAevP/8DxH/8t/Pt+2/+HcRn/5hpc0BAGBDBGEAwIP3b3oRv/vH+bb97R8i/ra32vYAALAZX226AXAfHz9+jGfPnt24zcuXL+Ply5drahEAWfQf/m6x7f/j30X8H//9atoCAPCQvX79Ol6/fn3jNh8/flxTa64ShLHVPn/+HD///PON23z69GlNrQEgqy5/v9rtAQD44tOnT7d+T98kQRhb7dGjR/H06dMbt3n8+PGaWgNAVu3+l6vdHgCALx4/fhzffPPNjdt8/PgxPn/+vKYWTRKEsdWePn0aP/3006abAUDG/Y//LOL/+r/n3/5/+GerawsAwEM2z/JEz54921jVmMXyAYAH738rRvzXfzHftn/1lxH/e3G17QEAYDMEYQDAg/ff/DriX/9P8237f/7PEY9/vdLmAACwIYIwACAX/tf/7vZt/rY833YAAGwna4TBGr1+/To+ffoUjx8/vnXONLA+xiZD/4sQLDOMS8ge4xKyx7hc3M5gMBhsuhGwqOHCet98881WLZa/re2Gh87Y/OJPnyO++lc3b/PHv4n41ZbWkz/0/j002zQuvbfIi20al5AX2zouN9lup2MAAAAAckEQBgAAAEAuCMIAAAAAyAVB2Ab0+/04PDy813Pb7fa92nB6ehqHh4exu7sbOzs7sbe3F5VKJbrdbqb2CQAAALAsgrAlStM0dnZ2br3t7e1FkiQL77tSqcTe3l50u924uLi4Uxu73W7s7u5GvV6PiIhWqxXn5+dxcnISvV4vDg8P4/DwMNI03eg+AQAAAJbtq0034CFpNptzbzsMjW6Tpmm8evUqTk9P79qskW63O6pEq1ar0Wg0Rv+XJEmUy+XY39+Pbrcb+/v7cXZ2FoVCYe37BAAAAFgFFWFL9OrVq7m2K5VKc1WEnZ6exv7+fvR6vfs2bVRRFvEloBoPrMa1Wq2I+DIFc7j9OvcJAAAAsCoqwpak2WxGmqZxfHx86/pfz58/v3V/vV4vSqVSHB8fj/Zfq9Xu3L5KpTKamnhTNdqwiqvdbke3241msxnVanVt++RheP36dXz69CkeP34cL1++3HRzViIPfYzITz/zIC+vZR76mYc+5kVeXss89DMPfcyLvLyWeehnHvrI3ewMBoPBphvxEOzt7UVExPn5+Ur23+v1Yn9/f/S40WjMHSb1+/1R+yIiLi8vb5ye2G63R5VbhUIhLi8v17LPRTx79ix+/vnn+Oabb+Knn366177WaVvbvag89DMPfYzQz4dknj7+6XPEV//q5v388W8ifpXhevKb+vkQ+heRj/drxHb1867vrW3q433koZ956GNEPvqZhz5G5KOfeehjxPb2c5Ptzvivetuh3W5Hv9+fe92vu7jPulonJyej+6VS6dZ9lcvl0f00TWdeoXIV+wQAAABYJUHYErx69SoKhUK8ePFi002ZaXwR/2KxONdzxtcwe/PmzVr2CQDwUP3b/7TpFgAAEYKwe+v1etHr9SJN09jd3Y29vb2o1WqZqXiaXmj/22+/net54+HWdF9WsU8AgG01T8j1z9vCMADIAkHYPU1Ph+z3+9FsNqNSqcTOzk5UKpWlXPXxrrrd7sTjea5WOWu78T6sYp8AANvoP/9DxL/89/Nt+y/+XcSnf1hpcwCAWwjC7qHf718Jhaa12+3Y39+/1xUf7+P9+/cTj+dda+zrr7+eePzhw4eV7hMAYBv9m17E7/5xvm1/+4eIv/V3QADYKEHYPSRJEo1GI05OTqJard5YGdVsNieu+rgu/X5/4vFdq7fGr4a5in0CAGyj//B3i23/HxfcHgBYrq823YBtV61WJx6naRrNZjNevXoVaZpO/F+v14vDw8PodDpra990aHVX431ZxT4BALbR5e9Xuz0AsFw7g8FgsOlGPFTtdjuOjo6uBD4nJydxfHy80L76/X7s7e2NHjcajSsh3Cw7OzsTj+d9ubvdbhweHo4el8vlaLVaK9vnov7yL/8y/vEfv8xDePTo/oWNf/3Xfx1/9Vd/de/93Objx4/x+fPnePToUTx9+nTlx9uUPPQzD32M0M+HZN4+/vzp5v1883jJDVuy2/q57f2LyMf7NWJ7+vn//L8R/98f59/+v/gq4r/9r77c35Y+3lce+pmHPkbko5956GNEPvqZhz5GrL+fv/3tb+Pv//7v772fz58/R0TEX/zFX8Qf/vCHe+9vEYKwFUvTNA4ODiYWhi8UCnF5ebnQfjYdhJVKpVEl2yr2uahf/epXo4EDAAAAbJ9Hjx7Fn/70p7Ue09TIFSsUCnF2dhb7+/ujMCxN0+h2u1EqldZy/GVMQRxfEH8V+1zUr3/96/j9738fg8FgqyrCAAAAYFstsyJsZ2cnfv3rXy+hVYsRhK3JDz/8MLFYfqfTWUsQ9uTJk6WEVk+ePFnpPhf1u9/97t7HBwAAAPLFVSPXpFgsTgRfy1pw/jZ3rbqaDrqmK8KWvU8AAACAVROErdH4+ljr8vz584nH81ZyXVxcTDweX59sFfsEAAAAWDVB2BolSTK6f59pgYsYn44ZMX8l2vn5+cTj8Wq2VewTAAAAYNUEYWs0HoSta1rgdPXWvKHVeJVXoVCYaPsq9gkAAACwaoKwNfrw4cPo/rqmSRaLxYnQ7f3793M9b7yt08HXKvYJAAAAsGqCsDUanxq4zmmBL168GN3v9XpzPWd8u3q9vpZ9AgAAAKySIGyN2u12REQcHx+v9bi1Wm10v9vt3rr9+DZJkswM7VaxTwAAAIBVEoStSbvdjn6/H4VCIb7//vuFnz/vlRlnKRaLE8HTMJC7TqvVGt2/rnJrFfsEAAAAWCVB2B11u93Y3d2NnZ2dODw8vHF6YL/fj6Ojo4iIePfu3Z0Wyp9ekH7RYKzRaIzuv3r16trt0jSNZrMZEV+mb1ar1bXuEwAAAGBVBGF31Gq1RmFUt9uN/f39iemCQ8P/e/LkSZyfn0exWFz4WGmaXqmievPmzUL7SJJkVJXV6/Xi9PR05nYHBwcR8eWqjuNVXOvaJwAAAMCq7AwGg8GmG7GNut3uzCs/FgqFKJVK8eTJk/jw4UP0er04Pj6O77//fqFKsDRN4+joKNI0vXENrlKpNJpuOU/I1u12o1KpRJqmUS6X4/vvv48kSeLDhw9Rr9ej1+tFsVhcqHJtFfsEAAAAWDYVYXdUKpXi/Pw8qtVqJEkyEfD0er24uLiI77//Pi4vL+Pk5GThAGhYPdXpdGIwGFx763Q60Wq15q40K5VKozb1+/04ODiI3d3dqFQq8eTJk2i1WnF2drZQe1exz212enoah4eHo6mze3t7UalU5rqowKrs7e3Fzs7OrWu5wUO1qXHZ6/WiVquNxuDw2PV6/V5rP0KWbfI8mMVzMGSB8yBkTxbPWbn53jgAlqLT6QwKhcIgIgalUmnQ6XQG5+fng1arNUiSZPTvl5eXa23X8fHxICIGETFotVprPTZs2qbG5eXl5aBcLo/G3nW3RqOx1OPCJm3yPJjVczBsmvMgZE9Wz1l5+t4oCIMl6HQ6ow+NarU6c5tisTiIiEGSJGv7UBtvVx4+0GDcpsbl5eXl6JeYeW7XtQ22ySbPg1k9B8OmOQ9C9mT1nJW3742CMLiny8vLUaKfJMm1252fn48+WEql0lrblZcPNBja5LgslUqDiBgUi8VBq9UanJ+fj/7KN/6XNmOTh2KT4y2r52DYNOdByJ6snrPy+L1REAb3NDzZz1PePV4ivupS8HK5PEiSZOJD7aF/oMHQpsZlo9EYRMTg+Pj42m3Oz89Hf+kb3gqFwr2OC5u0yfNgVs/BsGnOg5A9WT1n5fF7oyAM7mE8rY+IW0tXW63WWk74w19Czs7OcvWBBoPBZsdlkiRz/eVuuo0RMeh0Ovc6NmzCJsdbVs/BsGnOg5A9WT1n5fV7o6tGwj2cnJyM7pdKpVuvjFkul0f30zRdydU4+v1+1Gq1OD4+nvtqovCQbGpc9nq96Pf70Wq1bt02SZKJdg6fD9tmk+fBLJ6DIQucByF7snjOyvP3RkEY3EOz2Rzdn/fDI0mS0f03b94svU2VSiWKxeKVXy4gLzY1Lt+8eRPVavXWX2yGSqXSxONffvnlTseFTdrkeTCL52DIAudByJ4snrPy/L3xq003ALbV9F+tvv3227meVywWo9/vR0QsPdmv1+vR6/Xi/Px8qfuFbbHJcfndd99N/MIyzzHH7e3t3em4sCmbHG9ZPAdDFjgPQvZk8ZyV9++NKsLgjrrd7sTjeU/809stqwy81+vF6elpNBqNhX4JgYdkk+OyWCzO/VfwiC9l7je1AbJuk+Mta+dgyArnQcierJ2zfG8UhMGdvX//fuLxvCf+r7/+euLxhw8fltKeg4ODKJfLUa1Wl7I/2EZZG5c3Gf6Fb2h6ighk3SbH2zaNdVinbRobzoPkRdbGpe+NgjC4s+mT912T/WWUo1YqlYiI+OGHH+69L9hmWRqXtxn/ZSbPv4iwvTY53rZprMM6bdPYcB4kL7I0Ln1v/MIaYXBH0x9odzVdFr6odrsd7XY7Op3OQuXo8BBlZVzOo9FojO7X6/WVHw+WbZPjbZvGOqzTNo0N50HyIivj0vfGP1MRBnd01w+i6Q+di4uLe7WhUqlEtVpVTg6RjXE5j36/P1rn4eTkJLfrM7DdNjnetmWsw7pty9hwHiRPsjAufW+cJAiDDbtPsn9wcBBJkkz8RQ24v1X/JXx4meokSeL4+Hilx4Ks22RVloowmM15ELLH98blEYTBHS2rnPSu+zk9PY1erxetVmsp7YCHYNPjch69Xi+azWYUCoXodDorOw6s2ibH2zaMddiEbRgbzoPkzabHpe+NVwnCeJCazWbs7Ows9ba/vz9xjCdPniylrXfZT6/Xi3q9HicnJ1EsFpfSDli1hz4u53V0dBQREe/evTMVhK22yfG2DWMdNmEbxobzIHnje2P2CMLgju6ayE+XtN5lP5VKJYrFolJymLLJcTmPWq02+oucX0bYdpscb1kf67ApWR8bzoPkke+N2eOqkTxIpVJp6aWf0x88z58/Hy3yGfHlg2qeD6fpRQ739vYWasfp6Wn0+/0olUqjy9/eZPwD9NWrV/HmzZvR4++++y7K5fJCx4e7esjjch7NZjOazWY0Gg3jjgdhk+Mty2MdNinLY8N5kLzyvTF7BGE8SEmSrLzUenpKVr/fn+svW+fn5xOPF71qxy+//BIREd1ud6HnRXwpjR3/EE6S5EF9oJFtD3lc3qbb7UatVotGoxHVanWp+4ZN2eR4y+pYh03L6thwHiTPfG/MHlMj4Y6eP38+8bjf78/1vPGkvVAoWBsBliiL47LX68Xh4WGcnJz45Z8HZZPjLYtjHbIgi2PDeZC8y+K4zDtBGNxRsVicKGl9//79XM/78OHD6P70h+I8Tk5OYjAYzH0b/8BstVoT/ze8dDU8FJsal9fp9/txcHAQx8fH1mbgwdnkeMvaWIesyNrYcB4E3xuzSBAG9/DixYvR/fHS0ZuMb1ev15feJsi7rIzLfr8f+/v7Ua1W5/7lod/vx+np6VKOD+uwyfGWlbEOWZOVseE8CH+WlXHJF4IwuIdarTa6P8/c6/FtkiSxNgmsQBbGZZqmcXh4GC9evFjoL2iVSsXnAltlk+MtC2MdsigLY8N5ECZlYVzyZ4IwuIdisTjxodRut2/cfvyKebel+s1mM+r1+txzyIEvNj0u0zSN/f39SJJktO1tt263O1pI1eXk2SabHG+rPDZsM+dByJ5Nj0umDIB7OT8/H0TEICIGxWLx2u0uLy9H25VKpRv3WSqVRttGxODy8vLO7UuSZLSfVqt15/3ANtnkuCwWixPbLXJrNBr36TZsxCbH2yqODQ+B8yBkj++N2aEiDO4pSZJRYt/r9a5d1+Dg4CAivlzxYzzhn2W6XPbt27dLaCnkx6bG5f7+/tzrPszialpso02eB1dxbHgInAche3xvzA5BGCxBuVyOTqcThUIh6vV6VCqV6PV6kabpqNS71+tFsViMH3/8ceKqIbNM/79L5cLi1j0uh/u/K7/8s802eR5c9rHhoXAehOzxvTEjNl2SBg/NycnJoFgsDgqFwiAiBoVCYVAqlRYqL+10OoMkSQaFQmFwfHy8wtZCPhiXsD6bHG/LODY8RM6DkD3G5ebsDAaDwSaDOAAAAABYB1MjAQAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgF77adAMAAABgVXq9Xrx58ybSNI1Go7Hp5izdQ+8fLJuKMAAAAB6UbrcbtVot9vb2Yn9/P05PT6Pf72+6WRN2d3djZ2cnut3uws/Nev/SNI1KpRK9Xm/TTeGfDN8z+/v7sbe3Fzs7O7GzsxN7e3txeHgY9Xr9Tq/X6elp1Ov1FbR4dQRhAAAAPBjDL/XNZjNT4dC4brcbaZpGRESpVFrouVnvX7fbjd/85jeRJEkUi8XRvw2Dl9tuu7u70Ww2bz1Or9cbhYnX3fb39yMiotlszn38u9zGLXqs3d3d5b8IY05PT2N3dzcODw+j2WxGr9eLQqEQ5XI5yuVyFAqF6Ha7cXp6Gvv7+7G7uxunp6dz779arUav14u9vb1Mvh9n2RkMBoNNNwIAAACWqdvtxuHh4ehxqVSKTqezwRb9Wa1Wi2azGeVyOVqt1p32kcX+DauDGo1GVKvVK//fbrfj6OhoFAKOq1ard5ramabpKBgcKpVK0Wq1olAoTGzb6/WiXq/PrMJLkiSSJLnynOExIiIuLi6i3+9faf+sWOW2YzUajYVD0EVM/6wLhUKcnJzMfF3SNI1msxmvXr0abb9oG2u1Wrx9+zbevXs3CkCzShAGAADAgzRerZOFoGhod3c30jSNVqsV5XL5zvvJUv9uC8GGpgO8obOzszsHKGmajiqrCoVC/PjjjzMDraHhz3/cItFIv9+fmPp5eXl57fFmHes+fZ3H9HTFed8bwymt4+Hdba/nuEqlEu12e+X9uy9TIwEAAGBNer3enadFZlWz2Yx6vR7Hx8e3hialUmlm+Pfhw4c7H//i4mJ0/+Tk5MYQLCLi+fPnE49v235akiTx7t27uZ43fayIWGlIVK/X7xSCRXz5OXQ6nUiSZPRvtVpt7qmSrVYrisViHBwczKz6ywpBGAAAAKzJcPpfqVRaOIDJona7HbVaLYrFYpycnMz1nFnb3eeKl8PKrEKhMFf10jJ+7sOphosea5Wv+XCtr/Fj3WXqbafTmWjndVM8Z2m1WpGmaRwcHCx83HURhAEAAMCavH37NiK+TCPbdmmaxtHRUUTMDreukyTJlaqwXq9356tMDkO0eafwPXny5E7HmTZPRd+yjnWb4bTGcfNUx82SJMmVn2WlUpmrymv43OEaaVkkCAMAAIA1GJ8W+eLFi802ZgmG4UixWFx4mues4OzVq1cLtyFN01G1Uq1WW/j595EkycxF+TehXq9fCarmDQZn+f777ycep2k69+szDMBOT0/nriRbJ0EYAAAArMGbN28i4ssaUVkIT+6j3W6PQo7p0GQeSZJcCc/a7fbCa0sNrxZZLBYn1rZal/tc7GBZhld9HHefECziy7TK6b7Nu1bY+Gu77nByHoIwAAAAtlK73Y7Dw8PY3d2NnZ2d2N/fj1qtFv1+f9NNm6ndbkdExHfffTf39lnt33h10F3DoFlT5xatChtOi7xLGHdXaZpmajH46RAsYjlTb2dd3XPWsW56br/fn/s56yIIAwAAYKv0+/3Y29uLSqUSFxcXcXJyEp1OJ7777rvodruxt7c3V/VKpVIZhUyzbvv7+6PQqVarzdx2b29vrlCk3++P9nVbcLSs/kV8CZuu699tt/39/Zn77Ha7o/W87nMFxFKpdKWKa5HQpNvtzv0zXabpKzNu2rDScNysq1UuatZ013kX3x9/7iLrx63DV5tuAAAAAMyr1+uNApqTk5M4Pj4e/V+pVIrj4+Oo1WpzBRXDL/W1Wu1KADO970ajEbVabXTsYrEY7969m3uK47AaLEmSG6fwLbN/ETEK6ZIkiVqtdu2x379/fyVcuy7AGL/C46Jrg02r1+sT0+eG0/zmmdq36CL5y/Lhw4elBE3LMn2RgUKhsJSpt0mSRKFQmAh6513zazwg7ff70W63MzGNNEJFGAAAAFui3++PQqJyuTwREo1rNBoLVSo1Go0rgc75+fmV7T58+BARX8Kfs7OzhcKGYdXOTWHAqvpXKBTi/Pw8jo+Po1wuz7xNBxzVavXakGsY6kVEfPvtt3O3Y5ZqtXrl5zhPBVGapqN2rHMdqvtc3XIVZrVlmSHdrKtezjs1d/z90+l0ltam+xKEAQAAsBXG1z26LSxZdDrW9JSvZrM5UQmTpmnU6/UoFApzTw8bf+4wsLhpfbBV9e+29bNOT08nApVCoTBR9TVuOnhZxgL10xVd/X7/1sqj8UXy7zM9c17Dta4ODg5WfqxFzAqllnkhhln7mjcIG39d3r59u6wm3ZsgDAAAgMxrt9sT61LdFsAsWhUzK/w5OjqauJ+mabRarYWDhmEIUCgUrg1tVtW/i4uLG4Oifr9/ZZrlTUHfdEC1jCBsVlB3W9A3fK2WUQ2Wpumt66Xt7e1FrVbL1CL5EV9e32mzqrjuatbrO+/P4Ouvv554ThYu8hBhjTAAAAC2wPjVBOcJP+5SFVOtVqPVao3Cnna7PVqQvd1u3zhd8CbDYOnFixfXbrOq/p2cnNwYVk1fXfC2Pr5///5O7bhJoVCIarU6sU7b8Oc+q+29Xm8UqixrfbDbAr2Li4vMhWARs0OpZVaEzTIrfJtl+mfa6/WWEpzelyAMAACATOv3+xNT8lb5ZbrRaMTe3t7o8TAoSpLk2umCtxkGa9Oh09Aq+3fTvprN5pUpkbdVYq0qDKrX6zMvWDDrZz4MDZe1+PpwDbXb9Hq9ODo6ytQaYbNCr2W+RrP2NW/F2XTb5g3QVs3USAAAADJtFdPxrpMkyUQYlKbpaErkXQwXdC8UCtdWWq2zf0P9fv9K5dk80z5XFWYkSXLl5zO9TlvE5CL5t619tmzFYvHO74NVmRVKLfM1mrWvu1acZaWiThAGAABApp2dnU08XnVQdHx8vLQF2IdXi7xpuuG6+xdxdfpluVyea9rneJix7Cl402uVRcSVKrHhemtJkqxlkfxpswK7TbrPGl7zmLWu17zvz+mQ7pdffllKm+5LEAYAAECmbWKR7enKn+umNd5mWL1009Ui192/ZrM5UYVWKBTihx9+WHg/y67wKZVKV0KW8bXTIv68iP4yFsm/q2KxuPJ1uOY1Kwxc5vtp1muchXW+7kMQBgAAQKZNT89aR3A0vTbVrKsr3mY8bLppPat19i9N0yv9+OGHH+YOdlYdAE23LU3TUVXYKhbJv4uTk5Nb11Jbp+kwrN/vLyWknLUW2iLVcNPv6/GrSG6SIAwAAICtsuq1hnq9Xpyenl4Jr05PTxdaKH1YVbboou6r7F+lUpnYf7lcXqh9q64GqlarV8K2Yeg0vkh+ViqysmBWODW97txdzNrH4eHhnfeXlddMEAYAAECmTa81tOqKsEqlMpouOF0ZtsgUyeF6VjdNi4xYX//a7fa9p0ROt3UVod10tVe/3492uz2aZrrJaZHXabfba1sMvl6vTwSysy4a0Ol07n2cWftYpBJv+ueRlSmVgjAAAAAybfoL9DK+5F+nVqtFv98fTResVqsTU8/6/X6cnp7eup9utzsKAm6bTraO/qVpGkdHRxP/tsiUyKHpiqBVXEVyVrAzDCCztlh9xJfXulKprOyKmuOG1Yrj75lCoXClqm/6IgN3MV0Rdnx8vND7ZTrQff78+b3btAyCMAAAADJtOnwZVlotW7fbjWazGaVSaSJYmF44v16v31q1NXxOqVS6NTxYR/+Ojo4WnhJ5enp6JVCZDqFWUb02DCBnyWI1WK1Wi0KhsJaKp6Ojo0iS5Nrpo+PuE4ZNh72FQmFmQHmT8atEzmrzpgjCAAAAyLTp8CVN09E0uXnNU60zrDqaDr6SJLkSNNw2RXIYZs0zlXLV/et2uxP7m3dKZKfTuTIVcjrwWVV13nUXJljGIvnLrNwahqLXVakt+1i9Xm9mgJkkyZVpvPV6/U7TNdM0vXK1zlartXCQNR6SZqmKTxAGAABAps2a+jVd4TRtelH72wKB4SLyjUZj5hf+4+PjiQBoOEXtumMPj/fixYsbjxux+v5Nh3HzTIlM0zS63e7MKqfxti5jUfZZZk2BnKe6bh7LWsvr9PR09B64bh24ZR2rXq+PjnXdgvXVanUiKEzTdKE17YYODg4m2n1ycnKnIGv8vXGXdqzMAAAAADLu8vJyEBETt2KxOLi8vLyy7fn5+SBJkivbdzqdmftutVqDiBgkSXJjG87Ozq7s8+zs7Mp2x8fHo/Ztun/VanVim1KpNFd7yuXyICJmHn+6ravS6XTmev0WVSgUrvzsZvXzOq1Wa1AsFud6/qxjzXrPzHJ+fj5oNBpXXuvbDN9/w1u5XJ6rf5eXl4NSqTTx3EajMVdbZ+1ruI/bxtW67QwGg8EygzUAAABYhXa7PbOypFqtxuHhYVxcXMTZ2Vk0m82oVqvXrpFUrVZH08i63e6owub4+HjmWktDaZrG7u7uxL8VCoV49+7dxIL6e3t70e/34+TkJI6PjzfWv/G+DTUajZlVXmmaxsXFRZyfn0e73R5Na7suMhivUOp0Oiub+jb8WRYKhbi8vLzXvvr9ftRqtWur2IrFYjx58mRm1VmaptHv92euiVYsFuPs7GyhYw3XzJqeenpxcTF6LWZVk5VKpbmmo7bb7StVhY1G49qppc1mc2IqZaFQiFardefXtdlsjtZzu+m4G7HhIA4AAADm1ul0ZlbZjN+q1epgMBhM/FuSJIOTk5PB+fn5YDD4Um0zXdUT/1QxNat65uTkZGYV1vBWLBYH5+fng/Pz89G/DY+1if4NBoOZ/VvkVigUrm3neMXP8fHxwv2cV6PRuPcxhvtY1W28bas+1snJyUJ9Pz4+vvJ+KpVKg2q1OiiXy1cqwAqFwsLHmGX43staNdhgoCIMAACALXR6ehpv3ryJfr8faZpGoVCIUqkUtVptVMWyt7cX5XI5vvvuu4mKrVW3q16vR5IkcX5+fq/9ZLF/44ZVP8uo1rrJ7u5unJ2dreWqjA9Vt9uNVqsVHz58GFW3DSvSkiSJYrG4tPdRv9+Pvb29iIg4OzvbyHvzJoIwAAAAWJL9/f3o9Xq3TrN8KCqVSrTb7exNf2NjDg8Po9vtZnYMCMIAAABgCcbXEMtiJcyq7O3txcXFRfz4449Luaoj22u4Lt2sddOy4tGmGwAAAAAPwdu3byPiy0LjeQnBIr4slp+maRwdHW26KWxQmqZRqVQiSZJ49+7dpptzLUEYAAAALEGr1YqIiBcvXmy4JeuVJEmcnZ1Fu90eXSmQfEnTNPb39+PJkydxdnaW6cpAQRgAAADcU5qm0e12I+LLull5M5wK9/btW2FYzgxDsGEgmuUQLEIQBgAAAPc2DMGGV3fMo2EY9uHDhzg8PIw0TTfdJFas1+vFb37zm6jVatHpdDIfgkVYLB8AAADubTz02YYwYNVOT0+jXC5HkiSbbgor1G63I0mSrVoTTxAGAAAAQC6YGgkAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAv/PwDjjgJfZ4gAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-0.4, 0.4, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(array[\"ideal_state_770_tx\"]),\n",
    "    y=ak.to_numpy(array[\"z_mag_xEndT\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.ylim(5100, 5700)\n",
    "plt.xlabel(\"dx/dz(VELO)\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8awxwi5Pn2C3rmXjLOsUKAFh+BGEAAHjIyUDGySeCTt8J0alDrJ3qa9XvfAfMyskDzZfxcPRlnWIFACw/gjAAADzkZCDj5BNBJ6c+nJx6c6ovpkeAs5w8x87rM/H8mGIFAFwOBGEAAHjIyUDG6SeCTk19OL0N0Ym+mB4BLhenp1gBAMHBYfkAAHjIyUDGj0Os7Ux9+HGI9bS+lvXOd8CotZD08evT1+Dfp0W/VZ++1suzywAAy4+JMAAAPOTkFBeHWNvH9AhWQSh0crfYSS8hgrChZTy7zA47ZygCANzDRBgAAB5yeorrG9enT0RwiHVwpkeYKMJl8M4j6eWkM7W8PrvM7o1CrqwtX0AHAEHBRBgABJxlSZ/0J79Ylt9dzm/ZPj8/prg4xPrEqk6PzIKJIiw7p+4o6zU7/Th5oxAAgHuYCAOAgOtb0tXvTV7z8evSFQ+fPFvWSV+TrIXsPaFfxs9vGae4nOTH2WVO8To0BIJk1qDoxb/2ZjrTqSmueW4Usuj5iE5ychIPAJYZE2EAgKUzCK8mvUwLyvzi1BTDKgcyy3p2GQB/OX1HWSc4OcU1z41CvLKqk3gA4AYmwgAAsImzX+y77FNvnMUFzM6PO8pO4+QU1zLeKERa3kk8J6e/AWAWBGEAANiwrE8kVtkqT72FQt5upwUug2UMipwM5/y4UYid7YzLumVzGY8uABAMBGEAANiwrE8kAOA8yzi1uIx3lHUynHvxBem939ivNe1GIU5NIS/jJB4A+IkzwgAAsGGZz34BgNOW8Q6iy3hHWSfDuW8mpM9+yl6daTcKcfLssmWcxMNslu0O2MCqYyIMAAAbeCIBAItZxjvKOjnFNbhRyLTzEaXpNwpxcgp5GSfxgsKpc9DYRgo4iyAMAAAbeCIBIKic2mbpZFDkFKfDOaduFOLkdkant2zCPgIsYDmxNRIAABuWcUsPAHjByW2Wdu4U6+UdZQfhnB1OhXN2bhTi5BSyk1s2AeAyYCIMAAAblnFLj9OW8XBtAMFj946yTv03y6kpLic5OYW8jJN4AOAngjAAAGwIwhOJUGg5t2cQ0AE4j5f/zbIbzjnF6e2Myxj2Ocmps7iCgK8VQBAGAIBtl/2JxLJy6skugRqAVeHHFLLXYZ+TOIvLPr5WAEEYAACO8vqJBOGOfcs68QYApy3zFDL/3wGw6gjCsNI6nY5isdjENbu7u9rd3fWoIwDwFuEOAFxOyzqFzP93AEyzv7+v/f39iWs6nY5H3ZxFEIaV1u/3dXx8PHFNr9fzqBsAwAATAwDgvlXezgjg8ur1elOfp/uJIAwrbW1tTRsbGxPXhMNhj7oBAAwwMQAAABBM4XBYm5ubE9d0Oh31+32POhpHEIaVtrGxoaOjI7/bAICZMC0FAACAy8rO8USxWMy3qTGCMAAAPMa0FAAAAOCPNb8bAADAb+888rsDAAAAAF4gCAMAXGp2Qq5v1QnDAAAAgCBgayQAYCW980h6OTl5zdOPpO/+3F697/xMevGvpfAzC7cGALgAZyQCAPzGRBgAXFJeTzhZlvRJf/KLZdmr5dQU109a0h/+bO+av/+T9HbL3loAwHxCIenK2uSXEEEYlhxT5MBqYyIMAFaQ3aDoypr0jevu9yNJfUu6+r3Jaz5+ffoh8U5Ocf3i1/bqDPzy19Lf/91sjwEAAJfHMv6OBcBZBGEAsGIu+3a/eaa4Lgqvun+c7dqzrgcAYBRbP1fbZf8dC8AJgjAAWDFOBkXLyMkpruhnZqs163oAwOpzMrwKhaZPPmN5XfbfsQCc4IwwAFgx8wRFq8TJKa4XX5it1pdnXA8AWH2cW4aBy/47FoATBGEAsGIu+3Y/J6e4vpmQPvspe3U+92np5cRs1wYAAO7x+lD6y/47FoATbI0EgAks6+QQ+EnWQt7+pfiyb/d78QXpvd/YXz9piuvaM9KPv3JyqO00b36Vcz4AAJfTMp5dtoyH0l/237EAnGAiDAAmGNwJcdLLtKDMaX5s9/PyL7JOT3HZ+eX57Qx3fgIAXF7Ltv1z1kPpex+52s4QRyoAwUAQBgArxumgyO5fZL0KwwZTXHY4NcX1dUIwAAA8M8+h9F7gSAUgGAjCAGDFOBkULetfZJniAgDg8lrWQ+n9+GMcAO9xRhgArKBvXJ9+7pWdoGiVbxPOFBcA4DJbxnO9nLLMh9I79TsWgOXFRBgAXFJ2gqJl/YssAABBt2znejlp1Q+l549xwGojCAOAAFvmv8gCAIDLiUPpAfiJIAwAAmzV/yILAABWD4fSO8/LO3wDq44gDAACjL/IAgAAr/lxKP0qB0XLdodvYNURhAFAgPEXWQAA4Acn7xC9ykHRtJ6W9Q7fwCojCAOAAOM24QAAYFnZOZR+mYMiJwK6ee7wDWAygjCstE6no1gsNvFlf3/f7zaBpebkX2QBAAC8tKxBkVMBHXf4xira39+f+jy90+n41t9V364MOKDf7+v4+Hjiml6v51E3wGSWJfWtyWvWQst5K3RuEw4AAJbRPEHR3/+dO72MmiegO68v7vCNVdTr9aY+T/cTQRhW2tramjY2NiauCYfDHnUDTNa3pKvfm7zm49elK0sYhAEAACyjZQ2KnArouMM3VlE4HNbm5ubENZ1OR/1+36OOxhGEYaVtbGzo6OjI7zYAAAAA+GBZgyKnAroXX5De+439OtzhG8tgd3dXu7u7E9fEYjHfpsYIwgAAnnnnkfRy0u8u5rcWOpnam7YGAAB4Y1mDIqcCum8mpNf+xd42S+7wDdjDYfkAAEes8q3L7QqFpCtrk1+W8Yw3AAAuq28mpM9+yt5aL4OiF2cM3C4K6LjDN+A8JsIAAAub9c5IL/715F/UmLwCAAB2DIKib9Wnr/UyKHJykusb16d/ftzhG7CPIAwAsDCn7ow0EAo5d9MAQjUAAC63ZQyKvA7ouMM3YB9bIwEAC5vnzkheYTsjAADwIyiyE7wxyQV4jyAMALCwZb11OQAAwDJjkgvwHlsjASzEsqS+NXnNWoiJm8tuWW9dDgAAAACjmAjzgWEYSqfTttfm83ltbW0pFAopGo0qmUwqn8/LMIy5eyiXy0qn04pGowqFQtra2lI2m1Wz2Vyqmlh+fUu6+r3JL9OCMqw+p+6MBAAAAABuIghzkGmaCoVCU1+2trYUj8en1iuXy9ra2lK1Wh2GXqZpqtVqqVqtamtrS+VyeaYem82motGoisWiJKlWq6ndbqtUKqnVaimdTiudTss0TV9rAlgty3rrcgAAAAAYxdZIB1WrVdtrB6HRRdLptJrNpiKRiFKplOLxuAzDUKvVGpsEKxaLisfjymQyU6/ZbDaHk2i5XE6VSmX4sUGNZDKpZrOpZDKpw8NDRSIRz2sCWD3LeutyAAAAABjFRJiD9vb2bK0bBFsXKRaLajabKpVK6na7qtVqKpVKY5NWo7LZ7NRrmqY5XBePx8cCq1G1Wk3SyZbMaXXdqAlgdXFnJAAAAADLjokwh1SrVZmmqUKhMPX8r+3t7Qs/ZhiGyuWyGo2GUqnUuWsKhYLa7fbYBFqr1VIicfFeo2w2O9yaOGkabTDFVa/X1Ww2Va1WlcvlPKsJ4HLjzkgAAAAA/EQQ5pBSqaR4PH5mWmtWxWJRpVLpwhBs9HqjQViz2bwwCDMMY+zA+ps3b06sfevWLdXr9WE/54VWbtQEAAAAAABwE0GYA+r1ugzDuHBr4CwGU2XTRCKR4blhg7cvMhrOpVKpqWd0jZ43Zpqm6vX6mTPI3KgJAAAAIBjWQtLHr09fAwBOIwhzwN7eniKRyNSpKDsajYbttU+ePBm+Pmm75ejk2KTtk6NGQ7Z79+6dCa3cqAkAAAAgGEIh6coSBl0EdMDlx2H5C2q1Wmq1WjJNU9FoVFtbW8rn88NtgG4xTXN4PlcqlbowjGq1WmNv37hxw1b90XqnPxc3agIAAACA30Ih6cra5JcQQRiw0gjCFnT6kHjDMFStVpXNZhUKhZTNZs8ER064f/++pJMpq8FdGc8zeo7XYL0dp9eNfg5u1AQAAAAAAHAbQdgCTh8Yf556va5kMql8Pu/YdU3TVD6fVyKRUKPRmHg+18OHD8fennaW18Czzz479vbBwYGrNQEAAAAAANxGELaAeDyuSqWiUqmkXC43cTKqWq0qmUwufE3DMJRMJhWJRPTgwYOp01iDM7lGe7bj9Lp2u+1qTQAn3nnkdwcAAAAIMn4fxWXHYfkLyuVyY2+bpqlqtaq9vb3hGV4DrVZL6XR6pgPxR9XrdWWz2eHb0WhUpVJp4l0mT4dW8xr9XNyoCayydx5JL9vIue38UvGt+snZE9+4vnhfdnEoLAAAQDAs6++jgJeYCHNYJBJRoVBQt9tVrVY7s22w2WyqXC7brmeapsrlsra2tsZCsIFisXju+0cfP4/TfY/eodKNmsCysvvLwrR1Tz+Svvtze9f8zs+k3kf21jqBQ2EBAAAuP6d/H7Us6ZP+5BfLWrhtwHFMhLkok8kolUppZ2dn7GD4vb29iVNco5rNptrttlKp1IVnktXrdZXLZds15+HG9JYTNTudjmKx2MJ1dnd3tbu7u3AdXC6z/rLw4l9L4WfO//hPWtIf/myv1u//JL3dkv7+7y5ewxQXAAAAZuH076N9S7r6vcl1Pn5dusLvpJfK/v6+9vf3F67T6XQc6GY+BGEui0QiOjw8VDKZHIZhpmmq2WwqlUpNfXwmk1Emkxl7X7VaVbFYHAuSisWicrncmamrSCTiSOA0WteNmvPq9/s6Pj5euE6v11u4Bi4fJ39Z+MWvZ7v2L389+RePUIhfKgAAAGCf07+PIph6vZ4jz8H9RBDmkbt3744dlt9oNGwFYefJ5XJKpVJKJpNjgVS1Wj0zFba+vu5IaLW+vu5qzXmtra1pY2Nj4TrhcHjhGrh8nPxlofvH2WrNuh4AAACYhN9H4YRwOKzNzc2F63Q6HfX7fQc6mh1BmEcSiYRSqdRwa+OiB87H43E9ePBgLFx7+PDhmXXzTl2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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-0.25, 0.25, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(array[\"ideal_state_770_ty\"]),\n",
    "    y=ak.to_numpy(array[\"z_mag_xEndT\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.ylim(5100, 5700)\n",
    "\n",
    "plt.xlabel(\"dy/dz(VELO)\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bins = np.linspace( -300, 300, 50 )\n",
    "# sns.regplot(x=ak.to_numpy(array[\"x\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bins = np.linspace( -300, 300, 50 )\n",
    "# sns.regplot(x=ak.to_numpy(array[\"y\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bins = np.linspace( -1.0, 1.0, 50 )\n",
    "# sns.regplot(x=ak.to_numpy(array[\"dSlope_out\"]), y=ak.to_numpy(array[\"z_mag_x\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-1, 1, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(array[\"dSlope_xEndT\"]),\n",
    "    y=ak.to_numpy(array[\"z_mag_xEndT\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.ylim(5100, 5700)\n",
    "\n",
    "plt.xlabel(\"$\\Delta$dx/dz\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # import matplotlib.pyplot as plt\n",
    "# bins = np.linspace(-2000, 2000, 50)\n",
    "# sns.regplot(\n",
    "#     x=ak.to_numpy(array[\"x_l0\"]),\n",
    "#     y=ak.to_numpy(array[\"z_mag_x_fringe\"]),\n",
    "#     x_bins=50,\n",
    "#     fit_reg=None,\n",
    "#     x_estimator=np.mean,\n",
    "#     label=\"T1X1\",\n",
    "# )\n",
    "# sns.regplot(\n",
    "#     x=ak.to_numpy(array[\"x_l4\"]),\n",
    "#     y=ak.to_numpy(array[\"z_mag_x_fringe\"]),\n",
    "#     x_bins=50,\n",
    "#     fit_reg=None,\n",
    "#     x_estimator=np.mean,\n",
    "#     label=\"T2X1\",\n",
    "# )\n",
    "# sns.regplot(\n",
    "#     x=ak.to_numpy(array[\"x_l8\"]),\n",
    "#     y=ak.to_numpy(array[\"z_mag_x_fringe\"]),\n",
    "#     x_bins=50,\n",
    "#     fit_reg=None,\n",
    "#     x_estimator=np.mean,\n",
    "#     label=\"T3X1\",\n",
    "# )\n",
    "# plt.legend()\n",
    "# plt.xlabel(\"x [mm]\")\n",
    "# plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "# mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ideal_state_770_tx' 'dSlope_xEndT' 'dSlope_xEndT_abs' 'x_EndT_abs'\n",
      " 'ideal_state_770_tx^2' 'ideal_state_770_tx dSlope_xEndT'\n",
      " 'ideal_state_770_tx dSlope_xEndT_abs' 'ideal_state_770_tx x_EndT_abs'\n",
      " 'dSlope_xEndT^2' 'dSlope_xEndT dSlope_xEndT_abs'\n",
      " 'dSlope_xEndT x_EndT_abs' 'dSlope_xEndT_abs^2'\n",
      " 'dSlope_xEndT_abs x_EndT_abs' 'x_EndT_abs^2']\n",
      "intercept= 5092.708143256812\n",
      "coef= {'ideal_state_770_tx': 2018.6886668629327, 'dSlope_xEndT': 389.7888543955816, 'dSlope_xEndT_abs': 1464.867616153959, 'x_EndT_abs': 0.09763035198073229, 'ideal_state_770_tx^2': -4259.173364636334, 'ideal_state_770_tx dSlope_xEndT': 887.6220587366868, 'ideal_state_770_tx dSlope_xEndT_abs': -677.4689885623392, 'ideal_state_770_tx x_EndT_abs': -0.9313147953743464, 'dSlope_xEndT^2': 179.9382929971653, 'dSlope_xEndT dSlope_xEndT_abs': 88.39317926994904, 'dSlope_xEndT x_EndT_abs': -0.19078236037510163, 'dSlope_xEndT_abs^2': 2.3666592074995823, 'dSlope_xEndT_abs x_EndT_abs': -0.48044427953929886, 'x_EndT_abs^2': 5.0288654463924435e-06}\n",
      "r2 score= -0.09609938850755273\n",
      "RMSE = 356.01344182664485\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "features = [\n",
    "    \"ideal_state_770_tx\",\n",
    "    \"dSlope_xEndT\",\n",
    "    \"dSlope_xEndT_abs\",\n",
    "    \"x_EndT_abs\",\n",
    "]\n",
    "target_feat = \"z_mag_xEndT\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data, target, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "poly_features = poly.get_feature_names_out(input_features=features)\n",
    "# keep = [\n",
    "#     \"ideal_state_770_tx^2\",\n",
    "#     \"dSlope_xEndT^2\",\n",
    "#     \"dSlope_xEndT_abs\",\n",
    "#     \"x_EndT_abs\",\n",
    "# ]\n",
    "# remove = [i for i, f in enumerate(poly_features) if f not in keep]\n",
    "# X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "# X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "# poly_features = np.delete(poly_features, remove)\n",
    "print(poly_features)\n",
    "\n",
    "# lin_reg = LinearRegression()\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\"coef=\", dict(zip(poly_features, lin_reg.coef_)))\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.0, 1, 'LHCb'),\n",
       " expsuffix: Custom Text(0.0, 1.005, 'Simulation'))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(5150, 5300, 30)\n",
    "ax = sns.regplot(\n",
    "    x=y_test,\n",
    "    y=abs(y_test - y_pred_test),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    "    label=\"bla\",\n",
    ")\n",
    "ax2 = ax.twinx()\n",
    "ax2.hist(y_test,\n",
    "         bins=30,\n",
    "         range=[5150, 5300],\n",
    "         color=\"#2A9D8F\",\n",
    "         alpha=0.8,\n",
    "         align=\"left\")\n",
    "ax.set_xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "ax.set_ylabel(\"Mean Deviation [mm]\")\n",
    "ax2.set_ylabel(\"Number of Tracks\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_z_mag_x = np.median(array[\"z_mag_x_fringe\"])\n",
    "print(median_z_mag_x)\n",
    "params_per_layer = [[] for _ in range(12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_array(name, intercept, coef):\n",
    "    coef = [str(c) + \"f\" for c in coef if c != 0.0]\n",
    "    intercept = str(intercept) + \"f\"\n",
    "    code = f\"constexpr std::array {name}\"\n",
    "    code += \"{\" + \", \".join([intercept] + list(coef)) + \"};\"\n",
    "    return code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_diff_straight_l0\"] = (array[\"x_l0\"] - array[\"x\"] - array[\"tx\"] *\n",
    "                               (array[\"z_l0\"] - array[\"z\"]))\n",
    "array[\"x_l0_rel\"] = array[\"x_l0\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    # \"x_l0_rel\",\n",
    "    \"x_diff_straight_l0\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "poly_features = poly.get_feature_names_out(input_features=features)\n",
    "keep = [\n",
    "    # \"tx\",\n",
    "    # \"ty\",\n",
    "    # \"x_l0_rel\",\n",
    "    \"tx^2\",\n",
    "    # \"tx x_l0_rel\",\n",
    "    \"tx x_diff_straight_l0\",\n",
    "    \"ty^2\",\n",
    "    # \"x_l0_rel^2\"\n",
    "    \"x_diff_straight_l0^2\",\n",
    "]\n",
    "remove = [i for i, f in enumerate(poly_features) if f not in keep]\n",
    "X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "poly_features = np.delete(poly_features, remove)\n",
    "print(poly_features)\n",
    "\n",
    "lin_reg = LinearRegression()  # Lasso(alpha=0.004)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\"coef=\", dict(zip(poly_features, lin_reg.coef_)))\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l0\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[0] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l1_rel\"] = array[\"x_l1\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l1_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l1\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[1] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l2_rel\"] = array[\"x_l2\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l2_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l2\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[2] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l3_rel\"] = array[\"x_l3\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l3_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l3\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[3] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l4_rel\"] = array[\"x_l4\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l4_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l4\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[4] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l5_rel\"] = array[\"x_l5\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l5_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l5\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[5] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l6_rel\"] = array[\"x_l6\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l6_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l6\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[6] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l7_rel\"] = array[\"x_l7\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l7_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l7\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[7] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l8_rel\"] = array[\"x_l8\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l8_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l8\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[8] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l9_rel\"] = array[\"x_l9\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l9_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l9\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[9] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l10_rel\"] = array[\"x_l10\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l10_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l10\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[10] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[\"x_l11_rel\"] = array[\"x_l11\"] / 3000\n",
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"x_l11_rel\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "lin_reg = Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    dict(\n",
    "        zip(poly.get_feature_names_out(input_features=features),\n",
    "            lin_reg.coef_)),\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_l11\", lin_reg.intercept_, lin_reg.coef_))\n",
    "params_per_layer[11] = [lin_reg.intercept_] + list(lin_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "    \"tx\",\n",
    "    \"ty\",\n",
    "    \"dSlope_fringe\",\n",
    "]\n",
    "target_feat = \"z_mag_x_fringe\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "poly_features = poly.get_feature_names_out(input_features=features)\n",
    "keep = [\n",
    "    # \"tx\",\n",
    "    # \"ty\",\n",
    "    # \"dSlope_fringe\",\n",
    "    \"tx^2\",\n",
    "    \"tx dSlope_fringe\",\n",
    "    \"ty^2\",\n",
    "    \"dSlope_fringe^2\",\n",
    "]\n",
    "remove = [i for i, f in enumerate(poly_features) if f not in keep]\n",
    "X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "poly_features = np.delete(poly_features, remove)\n",
    "print(poly_features)\n",
    "\n",
    "lin_reg = LinearRegression()  # Lasso(alpha=0.01)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\"coef=\", dict(zip(poly_features, lin_reg.coef_)))\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(format_array(\"zMagnetParams_dSlope\", lin_reg.intercept_, lin_reg.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.optimize\n",
    "\n",
    "\n",
    "def parabola(x, a, b, c):\n",
    "    return a * x**2 + b * x + c\n",
    "\n",
    "\n",
    "params_1 = np.array([p[1] / params_per_layer[0][1] for p in params_per_layer])\n",
    "x = [array[f\"z_l{n}\"][0] - array[\"z_ref\"][0] for n in range(12)]\n",
    "print(params_1)\n",
    "print(x)\n",
    "plt.plot(x, params_1, \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_3 = np.array([params_per_layer[0][3] / p[3] for p in params_per_layer])\n",
    "x = [array[f\"z_l{n}\"][0] - array[\"z_ref\"][0] for n in range(12)]\n",
    "print(params_3**2)\n",
    "print(x)\n",
    "plt.plot(x, params_3, \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression, Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "feautures = [\"tx\", \"ty\", \"dSlope\"]\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data,\n",
    "    target,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    ")\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "lin_reg = LinearRegression()  # or Lasso if regularisation is needed\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))"
   ]
  }
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