{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import awkward as ak\n",
    "input_tree_md = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MD_selected_8520.root\": \"Selected\"})\n",
    "input_tree_mu = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MU_selected_8520.root\": \"Selected\"})\n",
    "# this is an event list of dictionaries containing awkward arrays\n",
    "array_md = input_tree_md.arrays()\n",
    "array_mu = input_tree_mu.arrays()\n",
    "array = ak.concatenate([array_md, array_mu])\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\"]\n",
    "def format_array(name, coef):\n",
    "    coef = [str(c)+\"f\" for c in coef if c != 0.0]\n",
    "    code = f\"constexpr std::array {name}\"\n",
    "    code += \"{\" + \", \".join(list(coef)) +\"};\"\n",
    "    return code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "bins = 50#np.linspace( -1.5, 1.5, 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"dSlope_fringe\"]), y=ak.to_numpy(array[\"CX_ex\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 50#np.linspace( -1., 1., 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"tx\"]), y=ak.to_numpy(array[\"CX_ex\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 50#np.linspace( -1., 1., 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"x\"]), y=ak.to_numpy(array[\"CX\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 50#np.linspace( -1., 1., 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"dSlope_fringe\"]), y=ak.to_numpy(array[\"DX\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 50#np.linspace( -1., 1., 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"tx\"]), y=ak.to_numpy(array[\"DX\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HD1djY6NWrlypVatWadWqVaEuFQAAWEDIw8v06dN19OhRLVmyRIcPH1Z2drbWrl2rjIwMSdLhw4e99nxpbGzU/fffr0OHDqlXr14aPny4PvjgA910002hLhUAAFiAzRhjIl1EZ3K5XHI4HKqurmb+CwAAFhHM9zf3NgIAAJYSlqXSAACEwtyCEpVVnZAkZSb31kszciJcEcKB8AIAsKyyqhPafaQm0mUgzLhsBADoEtxdawon/CC8AAAsqbjsmA4dP+l5vqeyVnMLSlRSdiyCVSEcCC8AAMsp2HJAt+YXqbah2ev4J7sqNS2/SCu3HIhQZQgHwgsAwFKKy47pkTU75OsiUYvbyEh6eM0ORmC6MMILAMBSXvlsn2JifN/ct1VMjE2vfL4/TBUh3AgvAADLqG9qUeFXR9Ti9j85t8VttG5nheqbWsJUGcKJ8AIAsIya+mZ1kFs83OZUe3Q9hBcAgGUkxPdQB1eMPGJsp9qj6yG8AAAsIz4uVhOGpSi2gwQTG2NT3nCn4uNiw1QZwonwAgCwlNnjBsrdwbUjt9to9lVZYaoI4UZ4AQBYymWZSXp8arZ8jb3Exthkk/T41GzlZCaFuzSECRcDAQCWc8eYDA11JmjWa8VeG9VNGJai2VdlEVy6OMILACDkQnH355zMJF35437a932d3MZoYHJf5d8x+pzfF9GP8AIACLlQ3f25M0IQrIc5LwCAsOLuzzhXhBcAQEhx92d0NsILACBkuPszQoHwAgAICe7+jFBhwi4AwK+zXSnUevdnfzdRbL37M0ubEQzCCwDAr7NZKdR69+eObqJ4+t2f2cofgeKyEQAgYIGuFOLuzwglwgsAoF1nu1KIuz8jlAgvAACfzmWlEHd/RigRXgAAbXTGSiHu/oxQIbwAANpoXSnkT+tKofZw92eEChcZAQBeOnOlEHd/RigQXgAAXs5mpZC/OSs5mUm64LxenuXWgwZw92ecG8ILAMBL60qhQAJMoCuFMpN7+/xv4GwQXgAAXlpXCn2yq9Lv7rixMTZNGJYS0EqhQHflBQLBhF0AQBusFEI0Y+QFALqAYO4/FEjb1pVCD/tYLh0bY5PbbVgphIghvABAF3D6/Yc62sI/0LasFEK0IrwACIm5BSXa932dvjt+UrJJzsR4DUzuq9/PzGnTxm1Mm9cQuPa28J8zbmCbgBFMW4mVQohOhBcAna647Jj+uPeo17/Wv/m+Tt98X+f5ojSSV5vTX+Nf9IEr2HLA5064n+yq1LqdR/T41GzdMSYj6LanY6UQoo3NmABvEWoRLpdLDodD1dXVSkxMjHQ5QJfma+SkvS/IVv6W4J4+l8LXl6iVBDMHpaP27b1WXHZMt+YXtdvXkmST9Id5uTJSwG0Jj4iEYL6/GXkBcFbOHF355vs63Zq/WX8q+8Hvef4WsLQuy314zQ4NdSZY+ks0mDkoHbVv77XWLfz9LWdu3cLfGBNwWyv3O7oHwguAoMwtKFFp+XFV1jS0ea2j4BIoq3+JBjuvxF97I/l87c7cjIC38P94R4VsAWw6F8h2/0A0ILwACFhx2TFt+vp7nWxyh/RzWr9wj59o1Hm9e4b0szpbsPNK/LX/eOcRn5/h7zVfjKRAJwgEst0/EGlsUgcgIAVbDujW/KKQB5dWRtIlSwo167Uv9Me934flM89Vcdmxduf7tLiNjE5dEispOxZQ+/b4e80Xm07NNQpEoNv9A5HEbyjQTQUzodTfl2yobdhdpQ27qzQsNVFLfjY8qi8lBTMHJSczKaD2HbFJfv9cWrfwN8Z06nb/QCQRXoBuKpgJpZ3xJXuuvjrs0v+XX6QnwrwSKdC9aOqbWgKeg7Ju56lLYoG070hHp7du4W8krevgUhPb/cMqCC9AlAh2ae25vPfscQMDnlAa6JdyuDy0ZoeciXZdNah/yEcIfK2o8tVPcwtKtLeyNuA+chuporo+pH3qawt/tvtHVxGWOS8vvviisrKyFB8fr9GjR+uzzz7z237jxo0aPXq04uPjNXDgQOXn54ejTCCiWkdCdh+p8QSNULx3aflx3Zpf5LWBnHRqEui0/CKt3HLA6/i8lVujJri0mr1iq4Y+/JHmFpR45o9Ip0LEhOc2avyzGzTnjZJz+ozWOT6B9FNZ1Ql9831dUO//9Me7A56H4k+MTXpr9hXqa/f+t+iEYSn6w7xcr1GqO8Zk6A/zcgNqC0SzkI+8vPvuu1qwYIFefPFFXXnllXrppZc0adIkffXVV7rooovatN+/f79uuukmzZkzRytXrtQf//hH/epXv1L//v11yy23hLpcICoEsi9IoM5chutribPke4+V4rJjKj4tHESbj3ce0cc7j6hnjxj172tXhave83Ocy469gUykfWjNDr3/5+/0LzcO8erfQB08dkIThqV0OA/Fn9Y5KmN/nKwrf9zP6/JWe1v452QmBdwWiFYh32H3iiuu0KWXXqrf/e53nmMXX3yxpk6dqqVLl7Zp/2//9m96//33tWvXLs+xefPm6c9//rOKioo6/Dx22IUVFZcd011n3PzuxuEpPi9PBLMLa0e73banjz1W6ef31u6KmohM0u0swezYe/rclpr6Zh2ta+wwVNh7xKix2X3WfXR55vnntDcOO+KiK4maHXYbGxu1detWPfDAA17H8/LytHnzZp/nFBUVKS8vz+vYjTfeqFdffVVNTU2Ki4vzeq2hoUENDX/7l6TL5eqk6oHwCGZfkGB2YT2XFUJ1DS36a0XNWZwZXQLdsdfXvZgC0dB8bsvGt5Yfb/e1WD8TpJmjgu4upOGlqqpKLS0tSklJ8TqekpKiiooKn+dUVFT4bN/c3KyqqiqlpqZ6vbZ06VI99thjnVs4ECaBXJ5o/eJtb6fV9nZh/cc3v5TNFvjmZGcr0HsVDXUm6JXP9+vjHRVhH82x2aSXNu1r80Xvb7fgcDg9nPSKi9XJphbP8wnDUjwrf2adMSrX+hrBBd1VWFYb2Wzes9KMMW2OddTe13FJWrRokRYuXOh57nK5lJ6efi7lAmET6L4gj/zPTu067ApqF9ZwfSHfcHGK/uHqgZKkf3zrSx1x/e1zz/ySzclMUn1Ti2rqm7Xj0HHd9fq5TaoNlNtIhV8d0Zw3ijX3mh8p+wKHPttTFZbdgiX/oyitr8eeNnt30ADveSjMUQG8hTS8JCcnKzY2ts0oS2VlZZvRlVZOp9Nn+x49eqhfv35t2tvtdtnt9s4rGgiTYPYF+eqw78uhkdx3pdWTPx+h/gmn/h/84tc3eMJJQnwPn0uZ4+NiFR8Xq+uGpuiJdpbuhkrhrkoV7qoM06f9TUd/Ti1uo9qGZg0a0FcxNpsyk3t7vd6Zy+aBriCkS6V79uyp0aNHq7Cw0Ot4YWGhxo4d6/Oc3NzcNu3XrVunnJycNvNdACurqW+OuiXIwfK1lXx8XKz6J9gD2oOldeluSqL3P0AuyzxfcZ2xjthi3pozRh/fezVhBehAyC8bLVy4UDNmzFBOTo5yc3P18ssvq7y8XPPmzZN06rLPoUOHtGLFCkmnVhYtX75cCxcu1Jw5c1RUVKRXX31Vb7/9dqhLBcIqIb6H3/ki0a6ztpLPyUzyOWJTXHZMt+YXWXq1UzB/vtxTCAhcyP9PmT59uo4ePaolS5bo8OHDys7O1tq1a5WRcWr1xOHDh1VeXu5pn5WVpbVr1+ree+/VCy+8oLS0ND3//PPs8QLLam95c3xc7Dnv8xFJnb2VfOvlpFaXZSa1uyOsVRgjXZ51vrYeOM49hYBOFPJ9XsKNfV4QbW78j02eJcxDUhL08b1Xe16L1OhC6wTSAQl21TU0q66xpeOTTjs30L1TOkNJ2bGIrVI6W6f30RBnQod/xuzXAkTRPi8AvJ25N4u/0YXWL8CLUxO1+0jNWY/OxNikGJtNzaedf/oqoGADVLiX6eZkJnmtUlpT+q2eXPvXsHz22Tqzj7inENC5GHkBQijQnXNLyo612ctjYrbTczfgcxmdsUm6LCtJP9Q1tntn5JVbDvj9cn1kyjDd/JO0dlcQhVs0jcacPoqVEN+j3TtPl5Qda7OUvPXPmOACBPf9TXgBQqS9nXPbu+xy+vb0Z34B+gsXgezCGsjlHX8BKlq/XFtHY+Jibbr33W3atKcqJPOHWvty9rgsvf/n784pgHS0lBzorggvhBdEWCCXYoKd5+AvXEhtd2E9m+DhL0BFu86YP2STZCQl2Huoxk9fEkCAzsecFyDCAt0595XP9wccLjq6G3Bn7MJq5f1FznV1kk3SjaeFFH8B5cyVUQDCi5EXoJPVN7Vo2CMfBbS/R4xN+mrJRL4IO1HrfJh1OysC+jMY6kzQ/XmDddWg/vw5ABHEyAsQQcHsnOs2p9rzpdl5zlydlBDfQzsOVXsFGpuka4f21+yrsnTlj/tHumQAQSK8AEFob8O50wWzcy67qobO6Zd2fAUaAiNgXfytCQShrOqEZ8O59gS6cy67qoYfc1WAroHwgm4jkFGTYJy54dzpZo8bqHU7j/g/v5O31weA7iKkd5UGoknrqMnuIzWeEBOM4rJjOnT8pOf5nspazS0oUUnZsTZtW1e++LovcmyMTTaJXVUB4CwRXtAt+Rs18aVgywHdml/ktY+KJH2yq1LT8ou0csuBNufcMSZDf5iXq7527wHOCcNS9Id5uWG5LxAAdEWEF3QLwYya+DrX1065ktTiNjKSHl6zw+d75WQm6YLzenmeDxpwav8VRlwA4OwRXtDlnc2oyelaN5zzp3XDOV8yk3trSEqChqQkaGD/PsEVDwBogwm76NI6GjWRTo2aDHUm+BwNqW9qUeFXRzpc9tziNlq3s0L1TS1tVrNYeddaAIhGjLygSzvXUZOz2XAOABBahBd0Wa2jJh3dZfj0UZMztW44Fwg2nAOA8CC8oMvqjFGT1g3nYjtIMLExNuUNd7IBGgCEAeEFXVZnjZrMHjdQ7g5SEBvOAUD4EF7QZXXWqAkbzgFAdCG8oEvrrFETNpwDgOhBeEGX1pmjJjmZSbryx/00aEBf/ah/H024OIUN5wAgAmzGBLlPepRzuVxyOByqrq5WYmJipMtBlCgpO6ZZrxV7bVQ3Mdup2VdlET4AIAoE8/3Nuk50C63b9O8+UiPpb9v0AwCsh/CCbiMzubfP/wYAWAvhBd0G2/QDQNfAhF0AAGAphBcAAGApXDZC1JlbUKKyqhOSTs1N4XIPAOB0hBdEnbKqE55VQQAAnInLRohq7q61DREAoBMQXhBVisuO6dDxk57neyprNbegRCVlxyJYFQAgmhBeEDUKthzQrflFXrvgStInuyo1Lb9IK7cciFBlAIBoQnhBVCguO6ZH1uyQr4tELW4jI+nhNTsYgQEAEF4QHV75bJ9iYnzdPvFvYmJseuXz/WGqCAAQrQgviLj6phYVfnVELW7/k3Nb3EbrdlaovqklTJUBAKIR4QURV1PfrA5yi4fbnGoPAOi+CC+IuIT4HurgipFHjO1UewBA90V4QcTFx8VqwrAUxXaQYGJjbMob7lR8XGyYKgMARCPCC6LC7HED5e7g2pHbbTT7qqwwVQQAiFaEF0SFyzKT9PjUbPkae4mNsckm6fGp2crJTAp3aQCAKMPkAUSNO8ZkaKgzQbNeK/baqG7CsBTNviqL4AIAkER4QZTJyUzSBef18tyYcdCAvsq/Y3SEqwIARBPCC6JOZnJvn/8NAIBEeEEUemlGTqRLAABEsZBO2P3hhx80Y8YMORwOORwOzZgxQ8ePH/d7zqxZs2Sz2bweY8aMCWWZAADAQkI68nL77bfr22+/1UcffSRJ+od/+AfNmDFD//u//+v3vIkTJ+q1117zPO/Zs2coywQAABYSsvCya9cuffTRR9qyZYuuuOIKSdLvf/975ebmavfu3RoyZEi759rtdjmdzlCVBgAALCxkl42KiorkcDg8wUWSxowZI4fDoc2bN/s9d8OGDRowYIAGDx6sOXPmqLKyst22DQ0NcrlcXg8AANB1hSy8VFRUaMCAAW2ODxgwQBUVFe2eN2nSJL355pv69NNP9eyzz6q4uFjXX3+9GhoafLZfunSpZ06Nw+FQenp6p/0MAAAg+gQdXhYvXtxmQu2Zj5KSEkmSzdZ2v1RjjM/jraZPn67JkycrOztbU6ZM0Ycffqivv/5aH3zwgc/2ixYtUnV1tedx8ODBYH8kAABgIUHPeZk/f75uu+02v20yMzP1l7/8RUeOHGnz2vfff6+UlJSAPy81NVUZGRnas2ePz9ftdrvsdnvA7wcAAKwt6PCSnJys5OTkDtvl5uaqurpaf/rTn3T55ZdLkr744gtVV1dr7NixAX/e0aNHdfDgQaWmpgZbKgAA6IJCNufl4osv1sSJEzVnzhxt2bJFW7Zs0Zw5c3TzzTd7rTQaOnSoVq9eLUmqra3V/fffr6KiIpWVlWnDhg2aMmWKkpOT9fOf/zxUpQIAAAsJ6SZ1b775pkaMGKG8vDzl5eXpJz/5iQoKCrza7N69W9XV1ZKk2NhYbd++XT/72c80ePBgzZw5U4MHD1ZRUZESEhJCWSoAALAImzHGRLqIzuRyueRwOFRdXa3ExMRIlwMAAAIQzPd3SEdeAAAAOhvhBQAAWArhBQAAWArhBQAAWArhBQAAWErI7ioNa5pbUKKyqhOSpMzk3nppRk6EKwIAwBvhBV7Kqk5o95GaSJcBAEC7uGyEdrm71hZAAIAugvACj+KyYzp0/KTn+Z7KWs0tKFFJ2bEIVgUAgDfCCyRJBVsO6Nb8ItU2NHsd/2RXpablF2nllgMRqgwAAG+EF6i47JgeWbNDvi4StbiNjKSH1+xgBAYAEBUIL9Arn+1TTIzNb5uYGJte+Xx/mCoCAKB9hJdurr6pRYVfHVGL2//k3Ba30bqdFapvaglTZQAA+EZ46eZq6pvVQW7xcJtT7QEAiCTCSzeXEN9DHVwx8oixnWoPAEAkEV66ufi4WE0YlqLYDhJMbIxNecOdio+LDVNlAAD4RniBZo8bKHcH147cbqPZV2WFqSIAANpHeIEuy0zS41Oz5WvsJTbGJpukx6dmKyczKdylAQDQBhMYIEm6Y0yGhjoTNOu1Yq+N6iYMS9Hsq7IILgCAqEF4gUdOZpKu/HE/7fu+Tm5jNDC5r/LvGB3psgAA8EJ4gZeXZuREugQAAPxizgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALAUwgsAALCUkIaXJ598UmPHjlXv3r113nnnBXSOMUaLFy9WWlqaevXqpWuvvVY7d+4MZZkAAMBCQhpeGhsbNW3aNN19990Bn/P000/rueee0/Lly1VcXCyn06kJEyaopqYmhJUCAACrCGl4eeyxx3TvvfdqxIgRAbU3xmjZsmV68MEH9Ytf/ELZ2dl64403dOLECb311luhLBUAAFhEVM152b9/vyoqKpSXl+c5Zrfbdc0112jz5s0+z2loaJDL5fJ6AACAriuqwktFRYUkKSUlxet4SkqK57UzLV26VA6Hw/NIT08PeZ0AACBygg4vixcvls1m8/soKSk5p6JsNpvXc2NMm2OtFi1apOrqas/j4MGD5/TZAAAguvUI9oT58+frtttu89smMzPzrIpxOp2STo3ApKameo5XVla2GY1pZbfbZbfbz+rzAACA9QQdXpKTk5WcnByKWpSVlSWn06nCwkKNGjVK0qkVSxs3btRTTz0Vks8EAADWEtI5L+Xl5dq2bZvKy8vV0tKibdu2adu2baqtrfW0GTp0qFavXi3p1OWiBQsW6De/+Y1Wr16tHTt2aNasWerdu7duv/32UJYKAAAsIuiRl2A88sgjeuONNzzPW0dT1q9fr2uvvVaStHv3blVXV3va/Ou//qtOnjypX/3qV/rhhx90xRVXaN26dUpISAhlqQAAwCJsxhgT6SI6k8vlksPhUHV1tRITEyNdDgAACEAw399RtVQaAACgI4QXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKT0iXYBVzC0o0b7v6+Q2RgOT++r3M3MiXRIAAN0SIy8BKC47pj/uPao9lbX65vs6Fe46orkFJSopOxbp0gAA6HYILx0o2HJAt+YXqbah2ev4J7sqNS2/SCu3HIhQZQAAdE+EFz+Ky47pkTU7ZHy81uI2MpIeXrODERgAAMKI8OLHK5/tU0yMzW+bmBibXvl8f5gqAgAAIQ0vTz75pMaOHavevXvrvPPOC+icWbNmyWazeT3GjBkTyjJ9qm9qUeFXR9Ti9jXu8jctbqN1OytU39QSpsoAAOjeQhpeGhsbNW3aNN19991BnTdx4kQdPnzY81i7dm2IKmxfTX2zOsgtHm5zqj0AAAi9kC6VfuyxxyRJr7/+elDn2e12OZ3OEFQUuIT4HoqxKaAAE2M71R4AAIReVM552bBhgwYMGKDBgwdrzpw5qqysbLdtQ0ODXC6X16MzxMfFasKwFMV2MOclNsamvOFOxcfFdsrnAgAA/6IuvEyaNElvvvmmPv30Uz377LMqLi7W9ddfr4aGBp/tly5dKofD4Xmkp6d3Wi2zxw2Uu4OhF7fbaPZVWZ32mQAAwL+gw8vixYvbTKg981FSUnLWBU2fPl2TJ09Wdna2pkyZog8//FBff/21PvjgA5/tFy1apOrqas/j4MGDZ/3ZZ7osM0mPT82Wr7GX2BibbJIen5qtnMykTvtMAADgX9ATNebPn6/bbrvNb5vMzMyzraeN1NRUZWRkaM+ePT5ft9vtstvtnfZ5Z7pjTIaGOhP0j299qSOuv43+TBiWotlXZRFcAAAIs6DDS3JyspKTk0NRi09Hjx7VwYMHlZqaGrbPPFNOZpK++PUNqm9qUU19sxLiezDHBQCACAnpnJfy8nJt27ZN5eXlamlp0bZt27Rt2zbV1tZ62gwdOlSrV6+WJNXW1ur+++9XUVGRysrKtGHDBk2ZMkXJycn6+c9/HspSAxIfF6v+CXaCCwAAERTS9b2PPPKI3njjDc/zUaNGSZLWr1+va6+9VpK0e/duVVdXS5JiY2O1fft2rVixQsePH1dqaqquu+46vfvuu0pISAhlqQAAwCJsxpgAt2KzBpfLJYfDoerqaiUmJka6HAAAEIBgvr+jbqk0AACAP4QXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKYQXAABgKSG9t1EktN7twOVyRbgSAAAQqNbv7UDuWtTlwktNTY0kKT09PcKVAACAYNXU1MjhcPht0+VuzOh2u/Xdd98pISFBNput09/f5XIpPT1dBw8e5MaPHaCvAkdfBYZ+Chx9FTj6KjCh7idjjGpqapSWlqaYGP+zWrrcyEtMTIwuvPDCkH9OYmIiv+QBoq8CR18Fhn4KHH0VOPoqMKHsp45GXFoxYRcAAFgK4QUAAFgK4SVIdrtdjz76qOx2e6RLiXr0VeDoq8DQT4GjrwJHXwUmmvqpy03YBQAAXRsjLwAAwFIILwAAwFIILwAAwFIILwAAwFIIL348+eSTGjt2rHr37q3zzjvPZxubzdbmkZ+f79Vm+/btuuaaa9SrVy9dcMEFWrJkSUD3brCKQPqpvLxcU6ZMUZ8+fZScnKx77rlHjY2NXm26ej/5kpmZ2eb354EHHvBqE0jfdRcvvviisrKyFB8fr9GjR+uzzz6LdEkRtXjx4ja/P06n0/O6MUaLFy9WWlqaevXqpWuvvVY7d+6MYMXhs2nTJk2ZMkVpaWmy2Wxas2aN1+uB9E1DQ4P+6Z/+ScnJyerTp49++tOf6ttvvw3jTxEeHfXVrFmz2vyejRkzxqtNuPuK8OJHY2Ojpk2bprvvvttvu9dee02HDx/2PGbOnOl5zeVyacKECUpLS1NxcbH+8z//U88884yee+65UJcfNh31U0tLiyZPnqy6ujp9/vnneuedd7Rq1Srdd999njbdoZ/as2TJEq/fn4ceesjzWiB91128++67WrBggR588EGVlpZq3LhxmjRpksrLyyNdWkQNHz7c6/dn+/btnteefvppPffcc1q+fLmKi4vldDo1YcIEzz3gurK6ujqNHDlSy5cv9/l6IH2zYMECrV69Wu+8844+//xz1dbW6uabb1ZLS0u4foyw6KivJGnixIlev2dr1671ej3sfWXQoddee804HA6fr0kyq1evbvfcF1980TgcDlNfX+85tnTpUpOWlmbcbncnVxpZ7fXT2rVrTUxMjDl06JDn2Ntvv23sdruprq42xnSvfjpdRkaG+Y//+I92Xw+k77qLyy+/3MybN8/r2NChQ80DDzwQoYoi79FHHzUjR470+Zrb7TZOp9P89re/9Ryrr683DofD5Ofnh6nC6HDm39OB9M3x48dNXFyceeeddzxtDh06ZGJiYsxHH30UttrDzdd32syZM83Pfvazds+JRF8x8tIJ5s+fr+TkZF122WXKz8+X2+32vFZUVKRrrrnGa1OfG2+8Ud99953KysoiUG34FRUVKTs7W2lpaZ5jN954oxoaGrR161ZPm+7aT0899ZT69eunSy65RE8++aTXJaFA+q47aGxs1NatW5WXl+d1PC8vT5s3b45QVdFhz549SktLU1ZWlm677Tbt27dPkrR//35VVFR49Zndbtc111zT7fsskL7ZunWrmpqavNqkpaUpOzu7W/bfhg0bNGDAAA0ePFhz5sxRZWWl57VI9FWXuzFjuD3++OMaP368evXqpf/7v//Tfffdp6qqKs/Qf0VFhTIzM73OSUlJ8byWlZUV7pLDrqKiwvMztzr//PPVs2dPVVRUeNp0x37653/+Z1166aU6//zz9ac//UmLFi3S/v379corr0gKrO+6g6qqKrW0tLTpi5SUlG7VD2e64oortGLFCg0ePFhHjhzRE088obFjx2rnzp2efvHVZwcOHIhEuVEjkL6pqKhQz549df7557dp091+5yZNmqRp06YpIyND+/fv18MPP6zrr79eW7duld1uj0hfdbuRF18T3M58lJSUBPx+Dz30kHJzc3XJJZfovvvu05IlS/Tv//7vXm1sNpvXc/P/JqGeeTyadHY/+fpZjTFex63YT74E03f33nuvrrnmGv3kJz/R7NmzlZ+fr1dffVVHjx71vF8gfddd+Pod6Y790GrSpEm65ZZbNGLECN1www364IMPJElvvPGGpw191r6z6Zvu2H/Tp0/X5MmTlZ2drSlTpujDDz/U119/7fl9a08o+6rbjbzMnz9ft912m982Z44ABGPMmDFyuVw6cuSIUlJS5HQ62yTP1uG2M1N/NOnMfnI6nfriiy+8jv3www9qamry9IFV+8mXc+m71hn8e/fuVb9+/QLqu+4gOTlZsbGxPn9HulM/dKRPnz4aMWKE9uzZo6lTp0o6NYKQmprqaUOfybMiy1/fOJ1ONTY26ocffvAaUaisrNTYsWPDW3CUSU1NVUZGhvbs2SMpMn3V7UZekpOTNXToUL+P+Pj4s37/0tJSxcfHe5YM5+bmatOmTV7zGNatW6e0tLRzCkmh1pn9lJubqx07dujw4cOeY+vWrZPdbtfo0aM9bazYT76cS9+VlpZKkucv1ED6rjvo2bOnRo8ercLCQq/jhYWF3f6L5HQNDQ3atWuXUlNTlZWVJafT6dVnjY2N2rhxY7fvs0D6ZvTo0YqLi/Nqc/jwYe3YsaPb99/Ro0d18OBBz99TEemrkEwD7iIOHDhgSktLzWOPPWb69u1rSktLTWlpqampqTHGGPP++++bl19+2Wzfvt3s3bvX/P73vzeJiYnmnnvu8bzH8ePHTUpKivnlL39ptm/fbt577z2TmJhonnnmmUj9WJ2uo35qbm422dnZZvz48ebLL780n3zyibnwwgvN/PnzPe/RHfrpTJs3bzbPPfecKS0tNfv27TPvvvuuSUtLMz/96U89bQLpu+7inXfeMXFxcebVV181X331lVmwYIHp06ePKSsri3RpEXPfffeZDRs2mH379pktW7aYm2++2SQkJHj65Le//a1xOBzmvffeM9u3bze//OUvTWpqqnG5XBGuPPRqamo8fxdJ8vy/duDAAWNMYH0zb948c+GFF5pPPvnEfPnll+b66683I0eONM3NzZH6sULCX1/V1NSY++67z2zevNns37/frF+/3uTm5poLLrggon1FePFj5syZRlKbx/r1640xxnz44YfmkksuMX379jW9e/c22dnZZtmyZaapqcnrff7yl7+YcePGGbvdbpxOp1m8eHGXWv7bUT8ZcyrgTJ482fTq1cskJSWZ+fPney2LNqbr99OZtm7daq644grjcDhMfHy8GTJkiHn00UdNXV2dV7tA+q67eOGFF0xGRobp2bOnufTSS83GjRsjXVJETZ8+3aSmppq4uDiTlpZmfvGLX5idO3d6Xne73ebRRx81TqfT2O12c/XVV5vt27dHsOLwWb9+vc+/l2bOnGmMCaxvTp48aebPn2+SkpJMr169zM0332zKy8sj8NOElr++OnHihMnLyzP9+/c3cXFx5qKLLjIzZ85s0w/h7iubMV18C1MAANCldLs5LwAAwNoILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFIILwAAwFL+f8+7TMzzBfn9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 50#np.linspace( -1., 1., 50 )\n",
    "sns.regplot(x=ak.to_numpy(array[\"x\"]), y=ak.to_numpy(array[\"DX\"]), x_bins=bins, fit_reg=None, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dSlope_fringe' 'tx dSlope_fringe' 'ty dSlope_fringe'\n",
      " 'tx^2 dSlope_fringe' 'tx ty dSlope_fringe' 'ty^2 dSlope_fringe']\n",
      "intercept= 0.0\n",
      "coef= {'dSlope_fringe': 2.3340814100448438e-05, 'tx dSlope_fringe': 8.310801914170572e-09, 'ty dSlope_fringe': -1.025087641029144e-06, 'tx^2 dSlope_fringe': 8.228862899700043e-06, 'tx ty dSlope_fringe': -1.5662848683486495e-06, 'ty^2 dSlope_fringe': -0.0003728935994887431}\n",
      "r2 score= 0.9860788549938552\n",
      "RMSE = 6.07178841864582e-07\n",
      "constexpr std::array cx_params{2.3340814100448438e-05f, 8.310801914170572e-09f, -1.025087641029144e-06f, 8.228862899700043e-06f, -1.5662848683486495e-06f, -0.0003728935994887431f};\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",
    "\n",
    "features = [\n",
    "    \"tx\", \n",
    "    \"ty\",\n",
    "    \"dSlope_fringe\",\n",
    "]\n",
    "target_feat = \"CX_ex\"\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=3, 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",
    "remove = [i for i, f in enumerate(poly_features) if (\"dSlope_fringe\" not in f ) or (\"dSlope_fringe^\" in f)]\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(fit_intercept=False)\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(\"cx_params\", lin_reg.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dSlope_fringe' 'tx dSlope_fringe' 'ty dSlope_fringe'\n",
      " 'tx^2 dSlope_fringe' 'tx ty dSlope_fringe' 'ty^2 dSlope_fringe']\n",
      "intercept= 0.0\n",
      "coef= {'dSlope_fringe': -7.054315710920644e-09, 'tx dSlope_fringe': 2.22196526392023e-11, 'ty dSlope_fringe': 6.104698063377923e-10, 'tx^2 dSlope_fringe': 2.85681046841909e-09, 'tx ty dSlope_fringe': 3.789175266592706e-10, 'ty^2 dSlope_fringe': 9.907774160587578e-08}\n",
      "r2 score= 0.97610245571401\n",
      "RMSE = 2.4251033068807623e-10\n",
      "constexpr std::array dx_params{-7.054315710920644e-09f, 2.22196526392023e-11f, 6.104698063377923e-10f, 2.85681046841909e-09f, 3.789175266592706e-10f, 9.907774160587578e-08f};\n"
     ]
    }
   ],
   "source": [
    "features = [\n",
    "    \"tx\", \n",
    "    \"ty\",\n",
    "    \"dSlope_fringe\",\n",
    "]\n",
    "target_feat = \"DX_ex\"\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=3, 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",
    "remove = [i for i, f in enumerate(poly_features) if (\"dSlope_fringe\" not in f ) or (\"dSlope_fringe^\" in f)]\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(fit_intercept=False)\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(\"dx_params\", lin_reg.coef_))"
   ]
  },
  {
   "cell_type": "code",
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