{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "input_tree = uproot.open({\"/work/guenther/reco_tuner/data/param_data_selected.root\": \"Selected\"})\n",
    "array = input_tree.arrays()\n",
    "array[\"dSlope_fringe\"] = array[\"tx_ref\"] - array[\"tx\"]\n",
    "array[\"z_mag_x_fringe\"] = (array[\"x\"] - array[\"x_ref\"] - array[\"tx\"] * array[\"z\"] + array[\"tx_ref\"] * array[\"z_ref\"] ) / array[\"dSlope_fringe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.hist(array[\"z_mag_x_fringe\"], bins=50,\n",
    "        range=[5150,5300], color='#2A9D8F', density=True)\n",
    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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(x=ak.to_numpy(array[\"tx\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)\n",
    "plt.xlabel(\"dx/dz(VELO)\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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(x=ak.to_numpy(array[\"ty\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)\n",
    "plt.xlabel(\"dy/dz(VELO)\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": null,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/guenther/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/pylabtools.py:151: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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HpM6JLEna39/vuj2xUCgokUhIknw+n/b398eyZj+YCAMAAACm1/Ud6YVC77q/u8TNcYBZ5OZ7eg7LH4FCoSDDMHqe+2VHc6qsF5/P13HgfrcQKpPJtD6PRqM9z+hqP2/MNE0VCkf/F8qJNQEAAADMB+4QDcAtBGEjsLGxIZ/Pp0uXLg29Vj/nZ+3t7bU+77bdsn0LZbftk+3aQ7YbN26MZU0AAAAAaOIO0QCcwBlhQyqXyyqXy5IODssPhUKKRqOKxWK27uQ4KNM0W+dzRaPRE8OoZm9NFy9etLV+OBxu3Z3y8PSWE2sCAAAAmB8LHunr13vXAMCoMRE2pMPbIQ3DUC6XUyKRkMfjUSKROBIcjcLNmzclHUxZ5fP5E+tKpVLH4/aprG4O17V/D06sCQAAAGB+eDzSqYXuH9wdGoATCMKGYBjGkVDosEKhoEgkorW1tZFd1zRNra2tKRwOq1gsdj2f6/bt2x2Pe53l1fToo492PN7e3nZ0TQAAAAAAAKexNXIIoVBIW1tbMk1TlUpFpVKptfXvsFwup+3tbe3s7Ax1TcMwFIvF5PP5dOvWrZ4h1OF+Bp3ear8bphNrAgAAAAAAOI2JsCElk0mlUiltbW2pUqlof39fmUzm2ICqXC4rFosNfK1CoaDl5WUZhiHTNOX3+5XNZrs+56Rgrl/N88icWhMAAAAAAMBpTISNmM/nUyqVUiqVUqFQ0OrqakfgUyqVlM1mlUqlbK1nmqZyuZy2traODaDS6bRu37594jlhg4ZNh4O89jtUOrHmoGq1moLB4NDrrK+va319feh1AAAAAACYVZubm9rc3Bx6nVqtNoJuBkMQ5qB4PK5oNKqVlZWOg+E3NjZsB2GlUkmVSkXRaPTEM8kKhUJf4dognJjeGsWajUZD1Wp16HXq9frQawAAAAAAMMvq9fpI3oO7iSDMYT6fTzs7O4pEIq0wzDRNlUolRaPRns+Px+OKx+Mdf5bL5ZROpzuCpHQ6rWQyeWTqyufzjSRwal/XiTUHtbCwoKWlpaHX8Xq9Q68BYDJZltSwutcseLgzFQAAANCL1+tVIBAYep1araZGozGCjvpHEDYm165dUyQSaT0uFou2grDjJJNJRaNRRSKRjkAql8sdmQpbXFwcSWi1uLjo6JqDWlpa0u7u7tDrAJhdDUs6/Vr3mq9fl04RhAEAAABdjepYoWAw6NpkGYflj0k4HO4IvoY9cD4UCunWrVsdf3b79u0jdYNOXR0Oug5PhI16TQBw09t33O4AAAAAwDgQhI3RMHeMPE44HO7YNnlcuHbhwoWOx3YnuQ4fZL+8vOzomgDgFDsh1wsFwjAAAOywLOmbRvcPq8eRBADgJrZGjlEoFGp9PoptgZJ0+fJlFQoFSccHUu3bMaWDsCwcDvdct1KpdDxun2ZzYk0AcMK9+9LL79qrffEd6cnvSN4zjrYEAMBU48gBANOOibAxag/CRrUtsD2AOm7Nw9NbdrdktodqPp+vo3cn1gQAJ7xVlj7/yl7tZ19K18u96wAAQHdMWQOYZARhY7S9vd36fNTbJKWjAZV0EJS1B2THnSN2nPZeD6/rxJoA4IT3Pumv/v0+6wEAmDccOQBg2hGEjVH71sBRbQtsn8Y6KVy7dOlS6/Ny2d64Q3tdOp0ey5oAMGr7XzhbDwDAPOn3yIH6fUfbAYCBEISNUfMsr1QqNbI1m+GSz+frODi/3draWuvzUqnUc832mlAodGxo58SaADBq/oecrQcAYJ5w5ACAWUAQNiaFQkGGYcjn8+nKlSsjW3djY0OSdO3atRNrwuFwR/DUDOROks/nW5+fNLnlxJoAMGpPPt5f/RN91gMAME84cgDALCAIG1CpVJLf75fH41EsFuu6PdAwDK2urkqSbt261fWg/Gw2q0gkonQ6fexdINs1a1Kp1InTYE1bW1utz5vh2XFM01Qul5N0sH0zmUyOdU0AGKXnwtLDD9irfeRB6fneN8AFAGBuceQAgFlAEDagfD7fCqpKpZIikUjHdsGm5tcWFxdVqVQ67vJ4mGmaSqfTKpfLymaz8vv9J05PJRIJZbNZZTIZZTKZnv2GQqHWVFZz/eOsrKxIOthq2T7FNa41AWCUzp6R3nzKXu3VpyXvGUfbAQBgqnHkAIBZQBA2oEQiceTPcrmc/H6/EomE1tbWFIlEFIvFlEwmtbOzo1Ao1HVNn893pKYZiCUSCaXTacViMfn9fkkHh+/3c95YPB5XsViUz+dTOp1WIpFQuVyWaZqtwK5cLiscDuvu3btdJ9ecXBMARunZ871rrsft1QEAMM84cgDALCAIG1A0GlWlUlEymVQoFOoIeMrlsvb29nTlyhXt7+8rk8nYDoB2dnaUSqUUDoePrGkYhhKJhO7evat8Pt8zWDup72ZPhmFoZWWlFbQtLi4qn89rZ2enr8DKiTUBYJx+SAgGAEBPHDkAYBZ4LMuy3G4C6FcwGFS1WlUgENDu7q7b7QCYYJYlNXr8L92CR/J4xtMPAADT7PqO9EL3+2RJkv7uEtPWAE7m5nt6JsIAADPN45FOLXT/IAQDAMAejhwAMO0IwgAAAAAAI8ORAwAm2Wm3GwAAAAAATIcFj/T1671rAGBSEYQBAAAAAGzxeKRTBF0AphhbIwEAAAAAADAXCMIAAAAAAAAwF9gaialWq9UUDAa71qyvr2t9fX1MHQEAAAAAML82Nze1ubnZtaZWq42pm6MIwjDVGo2GqtVq15p6vT6mbgAAAAAAmG/1er3n+3Q3EYRhqi0sLGhpaalrjdfrHVM3AAAAAADMN6/Xq0Ag0LWmVqup0WiMqaNOHsuyLFeuDAwhGAyqWq0qEAhod3fX7XYAAAAAAIBNbr6n57B8AAAAAAAAzAWCMAAAAAAAAMwFgjAAAAAAAADMBYIwAAAAAAAAzAXuGgkAgE2WJTV63GJmwSN5POPpBwAAAEB/CMIAALCpYUmnX+te8/Xr0imCMAAAAGAisTUSAIARevuO2x0AAAAAOAkTYQAA2GQn5HqhIJ1akJ4973w/AADYwdZ+APgdgjAAAGy4d196+V17tS++Iz35Hcl7xtGWAACwha39APA7bI0EAMCGt8rS51/Zq/3sS+l62dl+AAAYJbb2A5gXBGEAANjw3if91b/fZz0AAE6xu7WfMAzAPCAIAwDAhv0vnK0HAMAJ/W7tr993tB0AcB1BGAAANvgfcrYeAAAnsLUfADoRhAEAYMOTj/dX/0Sf9QAAOIGt/QDQiSAMAAAbngtLDz9gr/aRB6Xnw872AwCAHWztB4BOBGEAANhw9oz05lP2aq8+LXnPONoOAAC2sLUfADoRhAEAYNOz53vXXI/bqwMAYBzY2g8AnU673QAAANNiwSN9/frBHbj+7o70958cbCHxPyT968el585LPv4lHQAwQZ4LS69+YO/AfLb2A5gHBGEAANjk8UinPNLi/yb9xf9x8AEAwCRrbu1/odC7lq39AOYBQRimWq1WUzAY7Fqzvr6u9fX1MXUEAAAATJbnwpJH0kvvHj8Z9vADByEYW/sBjMLm5qY2Nze71tRqtTF1cxRBGKZao9FQtVrtWlOv18fUDQAAADB5PB7puYj0f/7v0ltl6f22rf1PPH6wHZJJMACjUq/Xe75PdxNBGKbawsKClpaWutZ4vd4xdQMAAABMrrNnpB9/9+ADAJzi9XoVCAS61tRqNTUajTF11MljWZblypWBIQSDQVWrVQUCAe3u7rrdDgAAAAAAsMnN9/QLY70aAAAAAAAA4BKCMAAAAAAAAMwFgjAAAAAAAADMBYIwAAAAAAAAzAWCMAAAAAAAAMwFgjAAAAAAAADMhdNuNwAAwLyxLKlhSffuS2/dkf7+E2n/C8n/kPTE49K/OX/wucfjdqcAAADAbCEIAwBgzBqWdPq1479W/LX0796X/nNcej4y3r4AAACAWcfWSAAAxuztO71rXijYqwMAAABgHxNhAACM0b370svv2qt98R3pye9I3jOOtgQAmEBsowcAZxCEAQAwRm+Vpc+/slf72ZfS9bL04+862xMAYPKwjR4AnMHWSAAAxui9T/qrf7/PegDAbGAbPQA4gyAMAIAx2v/C2XoAwPTrdxt9/b6j7QDATCEIAwBgjPwPOVsPAJh+g2yjBwDYQxAGAMAYPfl4f/VP9FkPAJh+bKMHAOcQhAEAMEbPhaWHH7BX+8iD0vNhZ/sBAEwettEDgHMIwgAAGKOzZ6Q3n7JXe/VpyXvG0XYAABOIbfQA4ByCMAAAxuzZ871rrsft1QEAZg/b6AHAOafdbgAAgHmz4JG+fv3grmB/d0f6+08OtrX4H5L+9ePSc+clH/+6DwBz67mw9OoH9g7MZxs9APTHY1mW5XYTQL+CwaCq1aoWFha0tLTUtXZ9fV3r6+tj6gwAAAAY3vUd6YVC77q/u8QEMYDJsrm5qc3Nza41tVpNjUZDgUBAu7u7Y+rsABNhmGqNRkPVarVrTb1eH1M3AAAAwGg8e753EMY2egCTqF6v93yf7iaCMEw1OxNhXq93TN0AAAAAo8E2egDTyuv1KhAIdK1pToS5ga2RmErNrZFujFECAAAAAIDBufmenrtGAgAAAAAAYC4QhAEAAAAAAGAuEIQBAAAAAABgLhCEAQAAAAAAYC4QhAEAAAAAAGAuEIQBAAAAAABgLhCEAQAAAAAAYC4QhAEAAAAAAGAunHa7AQAAMDjLkhqWdO++9NYd6e8/kfa/kPwPSU88Lv2b8wefezxudwoAAAC4jyAMAIAp1rCk068d/7Xir6V/9770n+PS85Hx9gUAAABMIoIwAACm2Nt3ete8UJBOLUjPnne+HwCYZ0zpAsDkIwgDAGBK3bsvvfyuvdoX35Ge/I7kPeNoSwAw15jSBYDJx2H5AABMqbfK0udf2av97EvpetnZfgBg3tmd0rVTBwBwBkEYAABT6r1P+qt/v896AIB9/U7p1u872g4A4AQEYQAATKn9L5ytBwDYx5QuAEwHgjAAAKaU/yFn6wEA9jGlCwDTgSAMAIAp9eTj/dU/0Wc9AMA+pnQBYDoQhAEAMKWeC0sPP2Cv9pEHpefDzvYDAPOMKV0AmA4EYQAATKmzZ6Q3n7JXe/VpyXvG0XYAYK4xpQsA08FjWZbldhNAv4LBoKrVqhYWFrS0tNS1dn19Xevr62PqDADG65uGdPq17jXX49JzkfH0AwDz6t59KfCGvQPzH3lQql7hHygAzKbNzU1tbm52ranVamo0GgoEAtrd3R1TZwdOj/VqwIg1Gg1Vq9WuNfV6fUzdAMD4LXikr18/eAP2d3ekv//k4NwZ/0PSv35ceu685GP7DQA4rjml+0Khdy1TugBmWb1e7/k+3U0EYZhqdibCvF7vmLoBgPHzeKRTHmnxf5P+4v84+AAAuOO5sOSR9NK7x0+GPfzAQQj27PlxdwYA4+P1ehUIBLrWNCfC3MDWSEyl5tZIN8YoAQAAgG7u3ZfeKkvvt03pPvH4wU1LmAQDAHff0zMRBgAAAAAjdPaM9OPvHnwAACYLd40EAAAAAADAXCAIAwAAAAAAwFwgCAMAAAAAAMBcIAgDAAAAAADAXCAIAwAAAAAAwFwgCHOBYRiKxWK2asvlstbW1rS8vCyPxyOPx6Pl5WWl02mZpjkxfUpSNptVLBaT3+9v9ZlIJFQqlRzsEgAAAAAAwB6CsBEyTbMVVnX7WF5eVigU6rlWIpFQJBJRLpeTYRitrxmGoWw2K7/fr1wu52qfklQqleT3+5VOpyVJ+XxelUpFmUxG5XJZsVhMsVjM8eAOAAAAAACgm9NuNzBL+gmlmqHRcUzTVCQS6Qi/TrK2tqadnR1tbW3Zvvao+pQOQrDm1FgymezoIxQKKR6PKxKJqFQqKRKJaGdnRz6fz/b1AQAAAAAARoWJsBHa2NiwVReNRrtOWiUSCRmGoXA43JquqlQqyufzSqVSR+pzuZwKhcLY+2xOrUkHoddJYVw+n5d0MMnWrAcAAAAAABg3JsJGJJfLyTRNpVKpnudqXbhwoes6pVJJqVRKmUym42vNCau1tTUlEgmVy+XW11ZXVxWPx8fWp3QQ2DW3O3abHGv2XSgUVCqVlMvllEwme/YKABgvy5IalnTvvvTWHenvP5H2v5D8D0lPPC79m/MHn3s8bncKAAAADMZjWZbldhOzYHl5WZJUqVSGXicUCqlYLHatMwyjdc2mYrGoaDQ6lj4PX39/f7/rlsdCodCaBvP5fNrf3x/q+sFgUNVqVYFAQLu7u0OtBQA48E1DOv1a95r/HJeej4ynHwAAAMwmN9/TszVyBAqFggzD6HmeVi/lclmGYbS2EnYTCoWOTIy1T4g52aekjmtHo9Ge5361T6uZptnXVk4AwHi8fad3zQsFe3UAAADAJCIIG4GNjQ35fD5dunRpqHVu3LihZDJp+zD5w9Nfv/3tb7vWj6pPqfPA/XA4bOs57eeN3bhxY+geAACjc+++9PK79mpffEeq33e0HQAYG8s6mIjd+3+l/8//Lf3Jf5Qu/oeD//v//b8P/pw9NAAwOzgjbEjlcrk1ieX3+xUKhRSNRhWLxWyd2dXu8uXLXQ+nP+xwAHV4q6RTfR6ePLt48aLtfpt3wmQiDAAmy1tl6fOv7NV+9qV0vSz9+LvO9gQA49CwTt4WXvy19O/eZ1s4AMwSJsKGdHiboWEYyuVySiQS8ng8Rw617yYcDtueBpPUOqi+qVuINso+S6WS7et2q7N7PQCA8977pL/69/usB4BJxbZwAJgvBGFDMAzjSCh0WKFQUCQS0dramiPXb3fSQfmj7vP27dsdj+2Gd48++mjH4+3tbVvPAwA4b/8LZ+sBYBKxLRwA5g9bI4cQCoW0tbUl0zRVqVRUKpWOhFNNuVxO29vb2tnZGdn124OkZDI5tj4PP3fQibBh71wJABgd/0PO1gPAJGJbOADMH4KwIR0OoEzTVC6X08bGxpGti+VyWbFYTMVicSTX3traan3e606Qo+zzpBCtX4evCwBwz5OPSx/+yn79E4871wsAjMsg28IJwgBgunksi3ugOKVQKGh1dfVI4JPJZJRKpYZa2zCM1uH4w67Xb58ej6fjsd0foVKppFgs1nocj8eVz+f7b1hSMBhUtVrVwsKClpaWBlqj3fr6utbX14deBwCm1b37UuANe5MRjzwoVa9I3jPO9wUATvqX/z/p9m4f9UHp//m/nOsHACbd5uamNjc3h16nVqup0WgoEAhod7eP/xCPABNhDorH44pGo1pZWek4GH5jY2PoICyTyUg62G447FpO9tnNKCbCGo2GqtXq0OvU6/Wh1wCAaXb2jPTmUwcHQvdy9WlCMACzgW3hANCfer0+kvfgbiIIc5jP59POzo4ikUgrZDJNU6VS6cTD7Xspl8vK5XLy+Xwj22bZT58+n28kIVY/d8g8yagmwrxe79BrAMC0ey4seSS99O7xk2EPP3AQgj17ftydAYAz2BYOAP3xer0KBAJDr9OcCHMDQdiYXLt2TZFIpPW4WCwOHIStrq5Kkm7dumX7oHq77PS5uLg4kiBscXFx6DWWlpbGPkYJALPK45Gei0j/5/9+cID0+58c3B3S/9DBm7/nw0yCAZgtz4WlVz+wvy38+bDzPQHAJBvVsULN447csODKVedQOBzuCJQGPXB+bW1N5XJZ+Xxe4fDo/5fYTp+DTnIdDs9GMREGABi9s2cODoP+4M8OzsL54M8OHhOCAZg1zW3hdrAtHABmA0HYGLUfFD+IXC6nXC6nra0txePxEXV1VK8+L1y40PHY7nTY3t5ex+PmYf8AAACAW54LS9fjB9u/j/PwA9JbCbaFA8CsYGvkGLVvY+x3W2CpVNLa2pq2traUTCZH3VqHXn22b52UDqbG7EynVSqVjseDbg0FAAAARoVt4QAwXwjCxqg9YOpnW2C5XFYsFlMmk3E8BJN693l4IsxuENY+Oebz+UZ+vhkAAAAwqOa28B9/1+1OAABOYmvkGG1vb7c+t7tN0jAMraysKJVKKZVKOdVah159hsPhjoDs9u3bfa97OEwDAAAAAABwGkHYGLVvDbSzLdAwDEUiESWTSWUyGVvXMAxD2f9/e/fz2+h15gv+UcU2yoihoipAA9XSisIAFfSqqMpdZGtq7sbu9kKUF3Y5K0tJEOACtZC60H9AQVoImOk7nYjGDNCxsyiLCwfxBToRs7x34xK9NZArZlOCgAa69JbgXBccWJwFhxz9pEiJP16Snw8gWD+Ozvuw7EMWv37OedfXr1xjRHt1Li4uNj+vVCptzXt83Orq6hWrAwAAALgaQVgflUqliIi2OruSJIn5+flYXFxsOwSLiCgUCtc+e6udOpeXl5ufl8vlS+c8PiabzTofDAAAAOg7Z4T1SalUimq1GplMJh49etRybJIkMTc3F9lsNlZXV6NarV46f7VabXZZtXNe13XrzOVykc/nmwFXqVRqeSfLra2t5ue6wQAAAIBBmKjVarVBFzGMyuVyFAqFSJIk8vl8rK2tXRhANbY4JkkSOzs7lwZVc3NzbW83PO30XSV7WWe1Wo3Z2dmIqAdjOzs7545LkiSmpqYior7Vcnt7+yoP7YSZmZnY29uL6enpePbs2bXnAwAAAPpjkO/pbY28oq2treZdEMvlcszNzZ3YLtjQ+Nnt27djd3e3pyFYRJy5q2Sv6oyob3FsdHpVKpULzyZ78803I6J+p8jjnWEAAAAA/SQIu6JCoXDme8ViMaampqJQKMTy8nLMzc3F/Px8LC0txc7OTmSz2Uvn7GYI1qs6j1tYWIjt7e3IZDKxurrafAxJkjTDtUqlErlcLv785z+fuNskAKOrVov47iji+f+K+D/+e8T//n9H/Oi/1v/5f/73+vf1pAMA0G+2Rl5DtVqNtbW1KJfL8fz580iSJDKZTNy+fTtyuVy8++67kc/nBx7+9KvO9fX1ePLkSVSr1eY17t+/H8vLyy3PD7sKWyMB0u27o4hX/qn1mH9diPhgrj/1AKPrxcuIX1ciPv8q4uCbiKnXI96+G/EgF3Hr5qCrA+A8g3xPLwhjKAnCANLt1zsRPyldPu6TxYj37vW+HmD01GoRH1cifv7biL/89ezPv/9qxC/fqQdiAKSLM8IAgJHx4mX9jWk7fvpZxOHLnpYDjKiPK/XA/bwQLKL+/Q+2In7zZX/rAiDdBGEAQFd9XLn4jelpX39b39IE0AmBOwBXJQgDALrqd191Nv7zDscDCNwBuCpBGADQVQff9HY8gMAdgKsShAEAXTX1em/HAwjcAbgqQRgA0FVv3+1s/FsdjgcQuANwVa8MugC4jv39/ZiZmWk55uHDh/Hw4cM+VQTAg1zEP/5be+f3vPFaxAe53tcEjJa370b84U/tjxe4A/TPxsZGbGxstByzv7/fp2rOEoQx1I6OjmJvb6/lmMPDwz5VA0BExK2bEf/yDxE/KV0+9lfvREze7HlJwIgRuAOk1+Hh4aXv0wdJEMZQu3HjRty5c6flmMnJyT5VA0DDg1zERET87Lfnv1H9/qv1EOy9e/2uDBgFAneA9JqcnIzp6emWY/b39+Po6KhPFZ00UavVagO5MlzDzMxM7O3txfT0dDx79mzQ5QBwgRcvIz6u1O/YdvBN/Zyet+7WuzO8MQWuo1aL+KRyeeD+vm4wgNQZ5Ht6HWEAQM/cuhnxix/XPwC6aWIi4sFcxN//ncAdgPYJwgAAgKElcAegEzcGXQAAAAAA9IMgDAAAAICxIAgDAAAAYCwIwgAAAAAYC4IwAAAAAMaCu0YCAKlWq0Uc1SJevIz4+MuI//ZVxME3EVOvR7x1N+L9e/XPJyYGXSkAAGknCAMAUu2oFvHKP53/s+3/GfFfPo/414WID+b6WxcAAMNHEAYApNpvvrx8zE9KEd+7EfHevd7XA1ydDk8ABk0QBgCk1ouXET//bXtjf/pZxNs/jJi82dOSgGvQ4QnAoDksHwBIrY8rEX/5a3tjv/424teV3tYDXE+7HZ7tjAOAqxCEAQCp9buvOhv/eYfjgf7ptMPz8GVPywFgTAnCAIDUOvimt+OB/tHhCUAaCMIAgNSaer2344H+0eEJQBoIwgCA1Hr7bmfj3+pwPNA/OjwBSANBGACQWg9yEd9/tb2xb7wW8UGut/UAV6fDE4A0EIQBAKl162bEL99pb+yv3omYvNnLaoDr0OEJQBoIwgCAVHuQi/hksd7xdZ43Xqv//L17/a0L6IwOTwDS4JVBFwDXsb+/HzMzMy3HPHz4MB4+fNinigDohffuRbz1w/pd5z7/qn520NTr9Y6RD3I6wWAY3LoZ8S//EPGT0uVjdXgCDK+NjY3Y2NhoOWZ/f79P1ZwlCGOoHR0dxd7eXssxh4eHfaoGgF66dTPiFz+ufwDD6UEuYiIifvbbiL/89ezPv/9qPQTT4QkwvA4PDy99nz5IgjCG2o0bN+LOnTstx0xOTvapGgAAWpmYiHgwF/H3f6fDE2BUTU5OxvT0dMsx+/v7cXR01KeKTpqo1Wq1gVwZrmFmZib29vZieno6nj17NuhyAAAAgDYN8j29w/IBAAAAGAuCMAAAAADGgiAMAAAAgLHgsHwAYCzUahFHtYgXLyM+/jLiv506pPv9e/XPJyYGXSkAAL0iCAMAxsJRLeKVfzr/Z9v/M+K/fB7xrwsRH8z1ty4AAPpHEAYAjIXffHn5mJ+UIr53I+K9e72vB4aFbkoARokgDAAYeS9eRvz8t+2N/elnEW//MGLyZk9LgqGhmxKAUeKwfABg5H1cifjLX9sb+/W3Eb+u9LYeGCbtdlO2Mw4ABk0QBgCMvN991dn4zzscD6Oq027Kw5c9LQcArk0QBgCMvINvejseRpVuSgBGjSAMABh5U6/3djyMKt2UAIwaQRgAMPLevtvZ+Lc6HA+jSjclAKNGEAYAjLwHuYjvv9re2Ddei/gg19t6YFjopgRg1AjCAICRd+tmxC/faW/sr96JmLzZy2pgeOimBGDUCMIAgLHwIBfxyWK94+s8b7xW//l79/pbF6SZbkoARs0rgy4AAKBf3rsX8dYP63fC+/yr+nlGU6/Xu1g+yOkEg9Ma3ZQfbF0+VjclAMNAEAYAjJVbNyN+8eP6B3C5B7mIGxMRP/0s4utvz/78jdfqIZhuSgCGgSAMAABoSTclAKNCEAYAAFxKNyUAo0AQxlDb39+PmZmZlmMePnwYDx8+7FNFAAAAML42NjZiY2Oj5Zj9/f0+VXOWIIyhdnR0FHt7ey3HHB4e9qkaAMbNi5cRvz61Veztu/UzlW7ZKgYAjKHDw8NL36cPkiCMoXbjxo24c+dOyzGTk5N9qgaAcfLJlxE/++zs4eF/+FPEo9/X77T3vsPDGTBhLQD9Njk5GdPT0y3H7O/vx9HRUZ8qOmmiVqvVBnJluIaZmZnY29uL6enpePbs2aDLAWDMfPJlxINP2xi36E56DEatVj/Y/ue/jfjLX8/+/Puv1sPaB7m+lwYAA31Pf6OvVwMAGHIvXtY7wdrx088iDl/2sho438eViJ+Uzg/BIurf/2Ar4jdf9rcuABg0QRgAQAc+rpzdDnmRr7+tb0uDfnrxst4J1g5hLQDjRhAGANCB333V2fjPOxwP1/Vx5eJOsNOEtQCMG0EYAEAHDr7p7Xi4LmEtAFxMEAYA0IGp13s7Hq5LWAsAFxOEAQB04O27nY1/q8PxcF3CWgC4mCAMAKADD3IRb7zW3tg3Xov4INfbeuA0YS0AXEwQBgDQgVs3I375Tntjf/VOxOTNXlYDZz3IRXz/1fbGCmsBGDeCMACADr1/L+KTxYs7w954rf7z9+71ty6IqIe1//IP7Y0V1gIwbl4ZdAEAAMPovXsRb/0w4uNK/a57B9/Uz1p66269w0a4wCA9yEVMRMTPfhvxl7+e/fn3X62HYMJaAMaNIAwA4Ipu3Yz4xY/rH5AmExMRD+Yi/v7vhLUAcJwgDAAgBV68jPj1qcDi7bv1zp5bAguuSFgLACcJwgAABuyTLyN+9lnE19+e/P4f/hTx6Pf1w/nft4VtbAhFAaB3BGEAAAP0yZcRDz69+Odff1v/+UQ4z2nU1Wr1bYw/P+dcrz/8KeIf/60eij5wl0cAuDJ3jQQAGJAXL+udYO346WcRhy97WQ2D9nEl4iel8w+3j6h//4OtiN982d+6AGCUCMIAAAbk48rZ7ZAX+frb+nY5RtOLl/VOsHYIRQHg6gRhAAAD8ruvOhv/eYfjGR4fVy7uBDtNKAoAVycIAwAYkINvejue4SEUBYD+EIQBAAzI1Ou9Hc/wEIoCQH+4ayRDbX9/P2ZmZlqOefjwYTx8+LBPFQFA+96+W78bYLveutu7WhgsoSgAo2JjYyM2NjZajtnf3+9TNWcJwhhqR0dHsbe313LM4eFhn6oBgM48yEU8+n17B+a/8VrEB7ne18RgCEUBGBWHh4eXvk8fJEEYQ+3GjRtx586dlmMmJyf7VA0AdObWzYhfvhPx4NPLx/7qnYjJm72uiEF5kIv4x39r78B8oSgAaTY5ORnT09Mtx+zv78fR0VGfKjppolar1QZyZbiGmZmZ2Nvbi+np6Xj27NmgywGAa/nNlxE//ez8zrA3XquHYO/d63dV9NuvdyJ+Urp83CeL/nsAYLgN8j29jjAAgAF7717EWz+M+LhSvxvgwTf1M6Deulvv/Om0E+zFy4hfn5rr7bv1rqNbuspS60EuYiIifvbb8zvDvv+qUBQArktHGENJRxgAnO+TLyN+9tnF3WW/fCfifUFKT3QrgHzxsnuhKACkkY4wAACu7ZMvW5839vW39Z9PhK6ibrsogPzDn+o3ROgkgLx1M+IXP65/AADddWPQBQAAcH0vXtaDmHb89LOIw5e9rGa8NALIi+7+2Qggf/Nlf+sCAM4ShAEAjICPKxcHMad9/W19Cx/XJ4AEgOEiCBuAarUa8/PzbY2tVCqxvLwcs7OzMTExERMTEzE7Oxurq6uRJMmVa1hfX4/5+fmYmppqzlkoFKJcLqdqTgCgPb/7qrPxn3c4nvMJIAFguAjCuihJkmZY1epjdnY2stnspXMVCoWYm5uLYrEY1Wq1+bNqtRrr6+sxNTUVxWKxoxrL5XJMTU3F6upqRERsbW3F7u5urK2tRaVSifn5+Zifn+8oZOvFnABAZw6+6e14zieABIDh4rD8LuoklGqERudJkiTm5uZOhF8XWV5ejp2dndjc3Lx0bLlcbnaiLS0tnfidbDYbCwsLMTc3F+VyOebm5mJnZycymUzf5wQAOjf1em/Hcz4BJAAMFx1hXfT48eO2xuXz+ZYdYYVCIarVauRyuWZ31e7ubmxtbcXKysqZ8cViMUqlUstrNjrMIuoB1UXB2dbWVkTUu84a4/s5JwBwNW/f7Wz8W22Of/Ey4p//R8R//n8i/tP/Vf/nf/0f9e8jgASAYaMjrEuKxWIkSRIrKyuXnv91//79lvOUy+VYWVmJtbW1Ez9rdFgtLy9HoVCISuX/P2Tiww8/jIWFhQvnLRQKza2JrbrRGtcolUpRLpejWCzG0tJS3+YEAK7mQS7i0e/bO6/qjdciPshdPu6TL+sHwZ+e8w9/ql/rl+9EvH/vKtWmx4uX9XO7Pv+q3q019Xo9VHyQi7h18/Lff/tu/c+jXe0GkABAb0zUarXaoIsYBbOzsxERsbu7e+15stlsbG9vtxxXrVab12zY3t6OfD5/6diDg4OW2xNLpVKzcyuTycTBwUFf5uzEzMxM7O3txfT0dDx79uxacwHAqPjky4gHn7YxbjHivUsCrG7OlVYXBX0R9bCwnaDvxcuImcftB5B7jyIm2wjYAGCUDfI9va2RXVAqlaJarbbsimpHpVKJarXa3ErYSjabPdMxdrxD7Ljj4/L5/KVndB3vLEuS5Nxtl72YEwC4nvfv1YOpN147/+dvvNZecPXiZT0gasdPP4s4HMJtko2g76IA6+tv6z//zZet57l1sx6YteNX7wjBAGDQBGFd8Pjx48hkMrG4uHiteZ48eRJLS0ttHyZ/uvvrP/7jP84dd/wQ/1yujX0QESfOMHvy5Elf5gQAru+9exHPHkX889sR//l/i/hPM/V//vPb9W6kdrq3Pq601+EUUR/36/P/X1xqdTvo61YACQD0njPCrqlSqTQ7saampiKbzUY+n4/5+fmWZ3ad59133215iP5ppwOo01slG/Ud96Mf/ajtuRt3rTzdvdWLOQGA7rl1M+IXP65/XMXvvups/OdfXX6t657F1c25rhL0Xfb43rsX8dYP63Mfr+utu/Xz2HSCAUA6CMKu6fR2yGq1GsVisdkxtbCwEI8ePWqra6rdzqqGxkH1DeeFaOVy+dIx5zk9rlKpNOvrxZwAQHocfNPd8d08dL8bc/Ui6Iu4fgAJAPSerZHXUK1Wz4RCp5VKpZibm4vl5eWeXP+48w7K/+KLL0583e62yx/84Acnvn769GlP5wQA0mPq9e6N79ZZXN2cq9tBHwAwPARh15DNZmNzczPW1tZiaWmpZWdUsViMubm5rl7/eJC0tLR07pjTYdlVu7eO3w2zF3MCAOnx9t3Oxr91wfhunsXVzbm6GfQBAMNFEHZNS0tLsbKyEpubm7G7uxsHBwextrZ2bpdUpVKJ+fn5rl17c3Oz+flFd6w8HVpd1fFtmL2YEwBIjwe5iw9+P+2N1+pnYJ2nm4fud3OubgV9AMDwEYR1WSaTiZWVlTg4OIitra0zgVi5XI719fVrX6darTYPrV9bW7uwK+uqYdPpup8/f97TOQGA9Lh1s37WVjt+9c7FB8Ff5Syui3Rzrm4FfQDA8HFYfg8tLCxEPp+PN99888SdFh8/fhwrKyvXmnttbS0i6tsNrztXO3rRvdWNOff392NmZuba8zx8+DAePnx47XkAYFS8fy9iIurbDM/rxHrjtXoI9l6Lg+m7eRZXN+dqBH0PPr18nlZBHwCMm42NjdjY2Lj2PPv7+12o5moEYT2WyWRiZ2cn5ubmmmFYkiRRLpfPPdy+HZVKJYrFYmQymdje3r70+t0InI53c/Vizqs6OjqKvb29a89zeHh47TkAYNS8dy/irR/WtyV+/lU9XJp6vb5V8IPc5QFRN8/i6va5Xt0I+gBg3BweHnblPfggCcL65KOPPjpxWP729vaVg7APP/wwIiL++Mc/XnpQ/e3bt7sSWt2+fbunc17VjRs34s6dO9eeZ3Jy8tpzAMAounUz4hc/rn906u27EX/4U/vjW53F1c25Gq4b9AHAuJmcnIzp6elrz7O/vx9HR0ddqKhzgrA+yeVykc/no1wuR8TVD5xfXl6OSqUSW1tbkctdfmDFVbuuTgddpzvCuj3nVd25cyeePXt27XkAgO57kIt49Pv2Drm/7Cyubs513HWCPgAYN906VmhmZmZgnWUOy++j694xslgsRrFYjM3NzVhYWGjrd+7fv3/i63Y7uU4fZD87O9vTOQGA0dOtQ/e7PRcAML4EYX10fBtjp9sCy+VyLC8vx+bmZiwtLbX9e8e3Y0a034m2u7t74uvj2zh7MScAMJrevxfxyeLFd2l847X6z9s5i6ubcwEA48nWyD46HoR1si2wUqnE/Px8rK2tdRSCRZzt3qpWq21tqTze5ZXJZE7U3os5AYDR1c2zuJzrBQBchyCsj54+fdr8vN1tktVqNd58881YWVmJlZWVjq+Zy+VO3OXxiy++aGtb5fFaTwdfvZgTABht3TyLy7leAMBV2RrZR8e3BrazLbBarcbc3FwsLS3F2tpaW9eoVquxvr5+4nuLi4vNzyuVSlvzHB+3urp65ue9mBMAAACglwRhfVQqlSIi2ursSpIk5ufnY3Fxse0QLCKiUCicCdmWl5ebnzfuWtnK8THZbPbc0K4XcwIAAAD0kiCsT0qlUlSr1chkMvHo0aOWY5Mkibm5uchms7G6uhrVavXSj3K53DzE/vR5Xblc7kTw1AjkLrK1tdX8/KLOrV7MCQAAANBLE7VarTboIoZRuVyOQqEQSZJEPp+PtbW1Cw+Mb2xxTJIkdnZ2Lj1Yfm5uru3thqdddFfJarUas7OzEVEPsXZ2ds79/SRJYmpqKiLq2ze3t7cvvFYv5mzXzMxM7O3txfT0dDx79uza8wEAAAD9Mcj39DrCrmhra6t5WHyjG+v4dsGGxs9u374du7u7PQ3BIuLCu0pms9lmV1alUjlzjljDm2++GRH1uzoe7+Lq15wAAAAAvSIIu6JCoXDme8ViMaampqJQKMTy8nLMzc3F/Px8LC0txc7OTmSz2Uvn7EUI1rCwsBDb29uRyWRidXW1eb0kSZqBXaVSiVwuF3/+858jk8lces1ezAkAAADQC7ZGXkO1Wo21tbUol8vx/PnzSJIkMplM3L59O3K5XLz77ruRz+dTGf6sr6/HkydPolqtNuu+f/9+LC8vx8LCQmrmvIitkQAAADCcBvmeXhDGUBKEAQAAwHAa5Hv6V/p6NRhzGxsbcXh4GJOTk/Hw4cNBlwP8f6xNSB/rEtLHuoT0sS47pyOMoTSsHWHDWjeMOmsT0se6hPSxLiF9hnVdumskAAAAAPSYIAwAAACAsSAIAwAAAGAsCMIAAAAAGAuCMAAAAADGwiuDLgCuY39/P2ZmZlqOefjwodvIAgAAQB9sbGzExsZGyzH7+/t9quYsQRhD7ejoKPb29lqOOTw87FM1AAAAMN4ODw8vfZ8+SIIwhtLXX38dERETExPxt3/7ty3HTk5O9qOkkbaxsRGHh4cxOTk5VN11w1p3xPDWPqx1D6th/fNWd38Na93Dalj/vNXdX8Na97Aa1j9vdffXsNadRpOTkzE9Pd1yzP7+fhwdHfWpopMmarVabSBXhmv43ve+F0dHR3Hjxo347rvvBl1O22ZmZmJvby+mp6fj2bNngy6nberuv2GtXd39pe7+Und/qbu/1N1f6u4vdfeXuvtL3Z1zWD4AAAAAY0EQBgAAAMBYEIQBAAAAMBYEYQAAAACMBUEYAAAAAGNBEAYAAADAWBCEAQAAADAWJmq1Wm3QRUCnJiYmmp9PT08PsJLO7O/vx9HRUdy4cSPu3Lkz6HLapu7+G9ba1d1f6u4vdfeXuvtL3f2l7v5Sd3+pu7+Gve5XX301vv32275eWxDGULpx40b4TxcAAACG140bN+K7777r6zVf6evVoEtef/31ePnyZXzve9+Lv/mbvxl0OQAAAECb/v3f/z2+++67uHnzZt+vrSMMAAAAgLHgsHwAAAAAxoIgDAAAAICxIAgDAAAAYCwIwgAAAAAYC4IwAAAAAMaCIAwAAACAsSAIAwAAAGAsCMIAAAAAGAuCMAAAAADGgiAMAAAAgLEgCAMAAABgLAjCoIeq1WrMz89HqVTqy/XW19djfn4+pqamYmJiImZnZ6NQKES5XO7L9SGt0r42Gs8VMAoGud7SvtZhUNK+NrwOMm68TxwsQRj0QJIkUSgUYnZ2Nsrlcjx//ryn1yuXyzE1NRWrq6sREbG1tRW7u7uxtrYWlUol5ufnY35+PpIk6WkdkDaDXhtJksTExMSlH7Ozs5HNZntSA/TLINfboNc6pNWg14bXQTjJ+8SUqAFdc3BwUFtZWalFxImPzc3Nnl1ze3u7eZ2lpaVzx+RyuVpE1LLZbO3g4KBntUCapGFtrK2tnXk+uOhjd3e369eHfhnkekvDWoc0SsPa8DoIdd4nposgDLpkbW2tls1ma/l8vm9PcAcHB7VMJtN88rrI7u5us5Z8Pt+TWiBN0rI2GjVc9mFdMswGud7SstYhbdKyNrwOgveJaWRrJHRBpVKJfD4fu7u7sb29HZubm325bqFQaLaxNtpdz5PNZmNhYSEi6u2xxWKxH+XBwKRhbRSLxUiSJFZWVmJ7e7vlx9bWVteuC/02yPWWhrUOaZSGteF1ELxPTKuJWq1WG3QRMGoqlUrMzc01v97c3IylpaWuXqNarcbs7Gzz64ODg8hkMheOL5VKUSgUIiIik8nEwcFBV+uBtEjL2mjUsLu725X5II0Gud7SstYhbdKyNrwOwlneJ6aDjjDogVZPNN2ytrbW/Dyfz196zUbSH1E/pLFfdyiBfkvD2iiVSlGtVlv+HzgYBYNcb2lY65BGaVgbXgfhfN4npoMgDIbU8bbVXC7X1u8cvxvPkydPul4TpEEa1sbjx48jk8nE4uLiteeCNBvkekvDWoc0SsPa8DoIg5OG54C0E4TBEKpUKie+/tGPftTW7x1/IhyHpJ/xk4a1UalUolKpRJIkMTU1FbOzs7G8vGzNMXIGud7SsNYhjdKwNrwOwuCk4TlgGAjCYAiVy+UTXx9P8Fs5Pe70EyUMuzSsjdPbQKrVahSLxSgUCjExMRGFQsHaYyQMcr2lYa1DGqVhbXgdhMFJw3PAMBCEwRD64osvTnzd7l7zH/zgBye+fvr0abdKglQY9NqoVqtn/gJyWqlUirm5uVheXr7SNSAtBrneBr3WIa0GvTa8DsJgDfo5YFi8MugCgM5Vq9UTX1816XcXH0bNoNdGNpuNzc3NSJIkdnd3o1wun6mpoVgsxtOnT2NnZ+dK14JBG+R6G/Rah7Qa9NrwOgiDNejngGEhCIMhdNFfKDqVJElX5oG0SMPaOH0L7CRJolgsxuPHj8/MW6lUYn5+Pra3t698PRiUQa63NKx1SKM0rA2vgzA4aXgOGAa2RsIQuuoT0+nW2OfPn1+/GEiRNK6NTCYTKysrcXBwEFtbW2euVS6XY319vWvXg34Z5HpL41qHNEjj2vA6CP2TxueANBKEwRgb9aQfrqpXa2NhYSH+/Oc/n7mV9ePHj3tyPRgGg3wt8joI5/M6CONt1F8fBWEwhNo99LBf80BaDMPayGQysbOzc+JNQJIklx4uDGkzyPU2DGsdBmEY1obXQeidYXgOSANBGCOpWCzGxMREVz/m5uYG/bCabt++nap5oB39WJfDtDY++uijE187H4VhM8j1NkxrHfppmNaG10HovmF6DhgkQRgMoasm9KdbXEc96Wf8DNPayOVykc/nm19363BT6JdBrrdhWuvQT8O0NrwOQvcN03PAILlrJCMpn8/H1tZWV+dM05PB/fv3o1KpNL9OkqSt+k4fejg7O9vt0uBC/ViXw7Y25ufnbQVhaA1yvQ3bWod+Gba14XUQumvYngMGRRDGSMpms5HNZgddRs+c3g5WrVbPHDp6nt3d3RNfH/+/cNBr/ViXw7Y2jv95jHoLOqNnkOtt2NY69MuwrQ2vg9Bdw/YcMCi2RsIQun///omv220lP97ymslkRjosZDwN29o4fp00dZ1COwa53oZtrUO/DNva8DoI3TVszwGDIgiDIZTL5U78ZeGLL75o6/eePn3a/Pz0kySMgmFbG8evOz8/37frQjcMcr0N21qHfhm2teF1ELpr2J4DBkUQBkNqcXGx+fnxfeCtHB+3urra9ZogDYZpbRxvQx/1FnRG0yDX2zCtdeinYVobXgeh+4bpOWBQBGEwpJaXl5uft3PI6PEx2WzWXzYYWcO0NkqlUkRErKys9O2a0E2DXG/DtNahn4ZpbXgdhO4bpueAQRGEQQ+cvv3sVRSLxVhdXb1wX/fpW043/iJxkeN36xuHlJ/x1cu1cdm67ESpVIpqtRqZTCYePXp07flgEAa53rwOwvm8DkJ6eZ+YEjWg67a2tmoR0fxYW1vr6Pfz+fyJ3z84ODh33O7ubnNMLpe7cL6Dg4PmuHw+31EtMIx6sTYuW5fb29u1TCbTnGtnZ6dlfY2xrcbBMBjEeuvltWEUeB2EdPI+MR0EYdBlBwcHtWw2e+IJqtWTz3mO/25E1DY3Ny8ce/zJ9KIn0lwuV4uIWiaTufDJEkZNt9fGZetyaWnpzJilpaUz8zTeKGSz2dru7u6VHx+kSb/XWy+vDaPC6yCki/eJ6SEIgy44ODioLSwsnEnoT3/k8/nawsLCpf/nq/F/yBof29vbLccf/z9wjfkPDg5q29vbzSe3XC43Vk9uUKt1d21cti63t7fPXfeZTKa2sLBQW1paal5zZWXFemTk9HO99fLaMEq8DsJgeZ+YToIwSKHt7e1aNputZTKZ2srKStu/t7a2Vsvlcs0nu0wmU8vn87Wtra0eVgvp14210c663N3drS0tLTXHNa6VzWZrCwsLta2trbH7iwbjp1/rrVfXhlHkdRBGg9fH7pio1Wq1AAAAAIAR566RAAAAAIwFQRgAAAAAY0EQBgAAAMBYEIQBAAAAMBYEYQAAAACMBUEYAAAAAGNBEAYAAADAWBCEAQAAADAWBGEAAAAAjAVBGAAAAABjQRAGAAAAwFh4ZdAFAAAAAKRdpVKJJ0+eRJIksbm5Oehyum7UH1+DIAwAAAAYGlNTU7GzsxPZbLbn1yqXy7G1tRXlcjmq1WpEROTz+Z5ftxNTU1ORJElsb293XNswPL5uE4QBAAAAQ2F9fb3ZsbS2ttbTa83Pz8fz58+jUqn09DrXUS6XI0mSiOg8wBqGx9cLzggDAAAAhkJjy16xWOz5tba3t2NnZye2t7d7fq2r2traioiIhYWFjn93GB5fLwjCAAAAgNQrlUrN7XtJkkSpVOrLddO8VfDTTz+NiIh33333ynOk+fH1giAMAAAASL3Hjx+3/HrcVCqVK2+LHGeCMAAAACDVKpXKmbOszvveOGlsE83n85HJZAZbzBARhAEAAACpdlH3VyMMGkeNbZGFQmHAlQwXQRgAAACQWsfPA2scDt/Qj0Pz0+j4tsjFxcXBFjNkBGEAAABAajW6wZaWlmJhYSGy2eyJn49jGPbkyZOIiMjlcrZFdkgQBgAAAKRWI+haXV098c+GtbW1a1+jVCrF/Px8TE1NxcTERMzNzcXy8nLzLpVp0+iQa/dukcP2+HpJEAYAAACkUrFYjCRJIp/PNzvBlpaWToypVqtRLpevNH+1Wo3Z2dkoFArx/PnzWFtbi+3t7Xj33XejXC7H7OxsrK+vXzpPoVBohkznfczNzTVDp+Xl5XPHzs7ONrc7XlZzY66FhYW+PL6IegB50eO77GNubq6ta/TDRK1Wqw26CAAAAIDTZmdno1qtxvb2duTz+eb3l5eXT2yJXFhYOHN+2GUqlUozoFlbW4uVlZUzY05fJ6J+l8bt7e1z5zxv/HlzH792LpeLP/7xj21vcVxfX4/V1dXIZrOxu7t74bhuP77G2Gw2G8vLy2e2qDZ88cUXZ8K10//+BumVQRcAAAAAcFq5XI5qtRrZbPZMiHI6wCmVSpEkSdthUrVabYZECwsL54ZEEfW7Uj59+jQqlUpb825ubp7pUDsvrHr69GlEtA7VLtI4H6xVN1ivHl8mk2kZvkWcvcPn0tJSakKwCFsjAQAAgBRqnP11+kywiHoXVS6XO/G9Tg7NLxQKZ65zWR3tOu/Olse3PCZJEqurq5HJZDruYkuSpBlatTofrFeP79GjRy1/vr6+fiJUy2Qysbm52fb8/SAIAwAAAFKl0VWVyWTOnAnWsLy8fOLr051IFymVSs2wJpfLXbjFr+H+/fttzdtwXvjz4Ycfnvg8SZLY2trq+I6Pn376afMap4PAhl49vufPn194zYj6v7PToWWnQV8/CMIAAACAVGl0KV0UgjV+djxISpKkeTfFVo4HZqfDtPN0GlY1aju+HbBUKkW5XI5isRilUunK2wUbwdLi4uKFY3r1+NbW1lrWfLwLLSJ9WyIbBGEAAABAaiRJ0tzmeNlWvNOB0GXb8KrV6omte5d1S13H6VoKhULzkPurbhdsnD12OnRq6OXjazVXsVg8syWy0y2l/SIIAwAAAFKjEYItLCxc2q10eite44D9ixw/xD6it0FYNps9EQYlSdLcEnkVjW63TCZzYadVPx9fQ7VaPdN5dpVtn/0iCAMAAABSo7G177JusIh60HP63KpWnUg7Oztnfr+XVlZWWp6r1YnG3SJbbTfs9+OLOLv9cmFhIZVbIhsEYQAAAEAqlEqlSJLk3LtCXuR0YNbq7pGtusV65XQH2EXbGi/T6AhrdbfIfj++YrF4ogstk8nERx991NcaOiUIAwAAAFKhk26whvO2UF4Uhj1//vzE1/0Ijk6fB3be3RUvczxsWlhYuHBcPx9fkiRnHsdHH32U2i2RDYIwAAAAYOAqlUpUKpXIZDItw57znL67ZLuH0SdJ0tF1OlWpVGJ9ff3M41lfXz9xuPxlGl1lnf659PLxFQqFE/MvLCx0XN8gCMIAAACAgbtKN1jD6XOqGqHaabdv3z7xda87wgqFQnO74Hl3kWzXp59+GhGtt0VG9O/xlUqlodsS2SAIAwAAAAYqSZLmGVinu7vakc1mzxzQ3gjWTo87bnt7u+NrtWt5eTmq1Wpzu+DS0tKJc8+q1Wqsr69fOk+5XG52Xl12CH0/Hl+SJPHhhx+e+N4wbIlsEIQBAAAAA9UIrZaWlq4cqJzuCmscvH/c/Pz8ia8bnVbdVi6Xo1gsRj6fP7Fd8PTB+aurq5d2bTV+J5/PX/pn04/H9+GHH3a8JXJ9fb3lTQz6SRAGAAAADFQjJOn0EPnj2jk0/3RH1fFOtHadPpD+PI1tj6eDr2w2G2tra+eOvUgjzGpnK2WvH1+5XD4xX7tbIre3t89s2xwUQRgAAAAwMMViMZIkiXw+f2ZrX6cuOzT/vIP4T3c4nXb6rLHLDqBvHCK/ubl5bgfXysrKicfZOFD/oms3rre4uNjyuhG9f3ynw7h2tkQmSRLlcvna/267RRAGAAAADEyjQ+o63WANpw/ar1arJw51j4gzHUxJksSbb755bgBUrVbPhD/nzdlQKpWiVCpFNpttedbZeVskzzvc/8mTJxERkcvl2t4y2qvHt7y8fGKO09s+L9I4TywtQdhErVarDboIAAAAYPyUy+XmuVbdCkpOn7mVz+fPHBpfKpXO3Wq4tLQU8/Pz8fz589jZ2YlisRhLS0sXnm+1tLTU7Do7/lhWVlbObIE8LkmSmJqaOvG9TCYTf/zjH08cqD87OxvVajXW1tZiZWWlxaM+qduP7/hja9jc3Dz331mSJPH8+fPY3d2NUqnU/PeRlvhJEAYAAAAMxPz8/IXdVd20u7t7JrQpl8vNbYwXaQRBExMTze9ls9lYXl6OhYWFyGazza6q0x1d+Xw+tra2znRyra+vx+bm5oWH5OdyuWbH2Ozs7IX1X6Zbjy8iYm5u7tyOtXZlMpk4ODi48u93kyAMAAAAGFvr6+vx5MmTqFarkSRJZDKZyOfzsby83Dx8fnZ2NhYWFuLdd9890bHV67pWV1cjm83G7u7uteZJ4+MbFEEYAAAAQMo0urAu22ZJZwRhAAAAACly/AyxnZ2dke/S6id3jQQAAABIkU8//TQi6mdrCcG6SxAGAAAAkCKNw/IXFxcHXMnoEYQBAAAApESSJM07aRYKhQFXM3oEYQAAAAAp0QjBGnd3pLsclg8AAACQEkmSND/PZDIDq2NUCcIAAAAAGAu2RgIAAAAwFgRhAAAAAIwFQRgAAAAAY0EQBgAAAMBYEIQBAAAAMBYEYQAAAACMBUEYAAAAAGNBEAYAAADAWBCEAQAAADAWBGEAAAAAjAVBGAAAAABjQRAGAAAAwFgQhAEAAAAwFgRhAAAAAIwFQRgAAAAAY0EQBgAAAMBYEIQBAAAAMBYEYQAAAACMBUEYAAAAAGNBEAYAAADAWBCEAQAAADAWBGEAAAAAjAVBGAAAAABjQRAGAAAAwFgQhAEAAAAwFgRhAAAAAIwFQRgAAAAAY0EQBgAAAMBYEIQBAAAAMBYEYQAAAACMBUEYAAAAAGPh/wWSI5X7Hno1agAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace( -1, 1, 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"dSlope_fringe\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)\n",
    "plt.xlabel(\"$\\Delta$dx/dz\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/guenther/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/pylabtools.py:151: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "bins = np.linspace( -2000, 2000, 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"x_l0\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=50, fit_reg=None, x_estimator=np.mean, label=\"T1X1\")\n",
    "sns.regplot(x=ak.to_numpy(array[\"x_l4\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=50, fit_reg=None, x_estimator=np.mean, label=\"T2X1\")\n",
    "sns.regplot(x=ak.to_numpy(array[\"x_l8\"]), y=ak.to_numpy(array[\"z_mag_x_fringe\"]), x_bins=50, fit_reg=None, x_estimator=np.mean, label=\"T3X1\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"x [mm]\")\n",
    "plt.ylabel(\"$z_{Mag}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tx^2' 'tx dSlope_fringe' 'ty^2' 'dSlope_fringe^2']\n",
      "intercept= 5205.144186525624\n",
      "coef= {'tx^2': -320.7206595710594, 'tx dSlope_fringe': 702.1384894815535, 'ty^2': -316.36350963107543, 'dSlope_fringe^2': 441.59909857558097}\n",
      "r2 score= 0.9604900589467942\n",
      "RMSE = 8.772908410819978\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",
    "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, target, test_size=0.2, 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",
    "\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"
   ]
  },
  {
   "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": {
      "image/png": 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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(x=y_test, y=abs(y_test-y_pred_test), x_bins=bins, fit_reg=None, x_estimator=np.mean, label=\"bla\")\n",
    "ax2 = ax.twinx()\n",
    "ax2.hist(y_test, bins=30,\n",
    "        range=[5150,5300], color='#2A9D8F', alpha=0.8, 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": [
    "\n",
    "array[\"x_diff_straight_l0\"] = array[\"x_l0\"] - array[\"x\"] - array[\"tx\"] * ( 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, target, test_size=0.2, 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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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(\"coef=\", dict(zip(poly.get_feature_names_out(input_features=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_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, target, test_size=0.2, 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",
    "\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",
    "def parabola(x, a,b,c):\n",
    "     return a*x**2 + b * x + c\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",
    "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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