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

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import awkward as ak\n",
    "input_tree_8 = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MU_selected_8520.root\": \"Selected\"})\n",
    "input_tree_9 = uproot.open({\"/work/guenther/reco_tuner/data/param_data_MU_selected.root\": \"Selected\"})\n",
    "    # this is an event list of dictionaries containing awkward arrays\n",
    "a8 = input_tree_8.arrays()\n",
    "a9 = input_tree_9.arrays()\n",
    "a8[\"dSlope_fringe\"] = a8[\"BX\"] - a8[\"tx\"]\n",
    "a8[\"z_mag_x_fringe\"] = (a8[\"x\"] - a8[\"AX\"] - a8[\"tx\"] * a8[\"z\"] + a8[\"BX\"] * a8[\"z_ref\"] ) / a8[\"dSlope_fringe\"]\n",
    "a9[\"dSlope_fringe\"] = a9[\"BX\"] - a9[\"tx\"]\n",
    "a9[\"z_mag_x_fringe\"] = (a9[\"x\"] - a9[\"AX\"] - a9[\"tx\"] * a9[\"z\"] + a9[\"BX\"] * a9[\"z_ref\"] ) / a9[\"dSlope_fringe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.0000e+00, 2.0000e+00, 1.1000e+01, 4.0000e+00, 1.0000e+01,\n",
       "        3.7000e+01, 4.8000e+01, 1.3100e+02, 2.9600e+02, 5.0700e+02,\n",
       "        7.9300e+02, 1.2100e+03, 1.6270e+03, 2.5630e+03, 4.3020e+03,\n",
       "        1.0385e+04, 7.2311e+04, 2.1642e+04, 9.7010e+03, 5.5550e+03,\n",
       "        3.6920e+03, 2.4630e+03, 1.6310e+03, 1.1600e+03, 8.0800e+02,\n",
       "        5.2300e+02, 3.9000e+02, 2.4400e+02, 1.6900e+02, 1.3100e+02,\n",
       "        1.0500e+02, 5.9000e+01, 3.4000e+01, 2.3000e+01, 1.2000e+01,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]),\n",
       " array([5000., 5020., 5040., 5060., 5080., 5100., 5120., 5140., 5160.,\n",
       "        5180., 5200., 5220., 5240., 5260., 5280., 5300., 5320., 5340.,\n",
       "        5360., 5380., 5400., 5420., 5440., 5460., 5480., 5500., 5520.,\n",
       "        5540., 5560., 5580., 5600., 5620., 5640., 5660., 5680., 5700.,\n",
       "        5720., 5740., 5760., 5780., 5800., 5820., 5840., 5860., 5880.,\n",
       "        5900., 5920., 5940., 5960., 5980., 6000.]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 10,
     "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 matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.hist(a9[\"z_mag_x_fringe\"], bins=50,\n",
    "        range=[5000,6000], alpha=0.5)\n",
    "plt.hist(a9[\"z_mag_x\"], bins=50,\n",
    "        range=[5000,6000], alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.7314211457299337e-05\n",
      "0.0068900126858267696\n"
     ]
    },
    {
     "data": {
      "image/png": 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5y/6Tk5NDai5+nyFDhig6OtqpuVhpaalzzY9lWUpNTb2CowQAAOGiX24vd7lcIa9t2+4ydrGLay5VfyU1f2zZsmUKBoPO0tDQcNmeAABAeOvToON2uyWpy4xKU1OTM/vidrvV3t6uQCBw2ZqTJ0922f+pU6dCai5+n0AgoI6Oji4zPRfExMQoPj4+ZAEAAObq06AzYsQIud1uVVVVOWPt7e3asWOHxo8fL0nKyMhQVFRUSE1jY6MOHDjg1Hi9XgWDQe3du9ep2bNnj4LBYEjNgQMH1NjY6NRs27ZNMTExysjI6MvDAgAAYarXDww8c+aMPv74Y+d1fX296urqlJCQoOHDh6uwsFAlJSUaOXKkRo4cqZKSEg0ePFh5eXmSJMuyNHfuXBUVFSkxMVEJCQkqLi7WmDFjNHXqVEnSqFGjNGPGDOXn52vdunWSpHnz5iknJ0dpaWmSpOzsbI0ePVo+n09PPvmkPv/8cxUXFys/P5+ZGgAAIOkKgs67776ryZMnO68XLVokSZozZ442btyoxYsXq7W1VQsWLFAgEFBmZqa2bdumuLg4Z5u1a9cqMjJSs2bNUmtrq6ZMmaKNGzcqIiLCqdm8ebMKCgqcu7Nyc3NDnt0TERGhN954QwsWLNCECRM0aNAg5eXlafVqbvEEAADnfa3n6IQ7nqMDXH08RwfA1zVgz9EBAAC4lhB0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABirz4POl19+qb//+7/XiBEjNGjQIN1yyy16/PHHde7cOafGtm0tX75cHo9HgwYN0qRJk3Tw4MGQ/bS1tWnhwoVKSkpSbGyscnNzdfz48ZCaQCAgn88ny7JkWZZ8Pp9Onz7d14cEAADCVJ8HnZUrV+q5555TWVmZDh8+rFWrVunJJ5/Ur371K6dm1apVWrNmjcrKyrRv3z653W5NmzZNLS0tTk1hYaEqKipUXl6uXbt26cyZM8rJyVFnZ6dTk5eXp7q6OlVWVqqyslJ1dXXy+Xx9fUgAACBMuWzbtvtyhzk5OUpJSdGGDRucsb/8y7/U4MGDtWnTJtm2LY/Ho8LCQi1ZskTS+dmblJQUrVy5UvPnz1cwGNTQoUO1adMmzZ49W5J04sQJpaamauvWrZo+fboOHz6s0aNHq6amRpmZmZKkmpoaeb1eHTlyRGlpad322tzcLMuyFAwGFR8f35enAcBXqN5QPNAt9Avv3NUD3QJw3ejN3+8+n9G566679J//+Z/66KOPJEn/8z//o127dul73/ueJKm+vl5+v1/Z2dnONjExMZo4caJ2794tSaqtrVVHR0dIjcfjUXp6ulNTXV0ty7KckCNJWVlZsizLqblYW1ubmpubQxYAAGCuyL7e4ZIlSxQMBnXrrbcqIiJCnZ2deuKJJ/SjH/1IkuT3+yVJKSkpIdulpKTo6NGjTk10dLSGDBnSpebC9n6/X8nJyV3ePzk52am5WGlpqVasWPH1DhAAAISNPp/ReeWVV/TSSy/p5Zdf1nvvvacXX3xRq1ev1osvvhhS53K5Ql7btt1l7GIX11yq/nL7WbZsmYLBoLM0NDT09LAAAEAY6vMZnccee0xLly7VAw88IEkaM2aMjh49qtLSUs2ZM0dut1vS+RmZYcOGOds1NTU5szxut1vt7e0KBAIhszpNTU0aP368U3Py5Mku73/q1Kkus0UXxMTEKCYmpm8OFAAAXPP6fEbniy++0De+EbrbiIgI5/byESNGyO12q6qqylnf3t6uHTt2OCEmIyNDUVFRITWNjY06cOCAU+P1ehUMBrV3716nZs+ePQoGg04NAAC4vvX5jM7MmTP1xBNPaPjw4brtttu0f/9+rVmzRn/zN38j6fzXTYWFhSopKdHIkSM1cuRIlZSUaPDgwcrLy5MkWZaluXPnqqioSImJiUpISFBxcbHGjBmjqVOnSpJGjRqlGTNmKD8/X+vWrZMkzZs3Tzk5OT264woAAJivz4POr371K/385z/XggUL1NTUJI/Ho/nz5+sf/uEfnJrFixertbVVCxYsUCAQUGZmprZt26a4uDinZu3atYqMjNSsWbPU2tqqKVOmaOPGjYqIiHBqNm/erIKCAufurNzcXJWVlfX1IQEAgDDV58/RCSc8RwfoW6Y+I6cneI4OcPX05u93n8/oAMB1aXtp9zWTl/V/HwBCEHQAoA9Uf/JZtzXeyVehEQAh+PVyAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABirX4LO7373O/31X/+1EhMTNXjwYH3nO99RbW2ts962bS1fvlwej0eDBg3SpEmTdPDgwZB9tLW1aeHChUpKSlJsbKxyc3N1/PjxkJpAICCfzyfLsmRZlnw+n06fPt0fhwQAAMJQnwedQCCgCRMmKCoqSm+++aYOHTqkp556SjfeeKNTs2rVKq1Zs0ZlZWXat2+f3G63pk2bppaWFqemsLBQFRUVKi8v165du3TmzBnl5OSos7PTqcnLy1NdXZ0qKytVWVmpuro6+Xy+vj4kAAAQply2bdt9ucOlS5fqv//7v/XOO+9ccr1t2/J4PCosLNSSJUsknZ+9SUlJ0cqVKzV//nwFg0ENHTpUmzZt0uzZsyVJJ06cUGpqqrZu3arp06fr8OHDGj16tGpqapSZmSlJqqmpkdfr1ZEjR5SWltZtr83NzbIsS8FgUPHx8X10BoDrV/WG4oFu4Zrmnbt6oFsAjNCbv999PqPz+uuva9y4cfrhD3+o5ORkjR07Vs8//7yzvr6+Xn6/X9nZ2c5YTEyMJk6cqN27d0uSamtr1dHREVLj8XiUnp7u1FRXV8uyLCfkSFJWVpYsy3JqLtbW1qbm5uaQBQAAmCuyr3f4ySef6Nlnn9WiRYv005/+VHv37lVBQYFiYmL04x//WH6/X5KUkpISsl1KSoqOHj0qSfL7/YqOjtaQIUO61FzY3u/3Kzk5ucv7JycnOzUXKy0t1YoVK772MQLXI2ZrAISjPp/ROXfunO644w6VlJRo7Nixmj9/vvLz8/Xss8+G1LlcrpDXtm13GbvYxTWXqr/cfpYtW6ZgMOgsDQ0NPT0sAAAQhvo86AwbNkyjR48OGRs1apSOHTsmSXK73ZLUZdalqanJmeVxu91qb29XIBC4bM3Jkye7vP+pU6e6zBZdEBMTo/j4+JAFAACYq8+DzoQJE/Thhx+GjH300Ue66aabJEkjRoyQ2+1WVVWVs769vV07duzQ+PHjJUkZGRmKiooKqWlsbNSBAwecGq/Xq2AwqL179zo1e/bsUTAYdGoAAMD1rc+v0Xn00Uc1fvx4lZSUaNasWdq7d6/Wr1+v9evXSzr/dVNhYaFKSko0cuRIjRw5UiUlJRo8eLDy8vIkSZZlae7cuSoqKlJiYqISEhJUXFysMWPGaOrUqZLOzxLNmDFD+fn5WrdunSRp3rx5ysnJ6dEdVwAAwHx9HnS++93vqqKiQsuWLdPjjz+uESNG6Omnn9aDDz7o1CxevFitra1asGCBAoGAMjMztW3bNsXFxTk1a9euVWRkpGbNmqXW1lZNmTJFGzduVEREhFOzefNmFRQUOHdn5ebmqqysrK8PCQD6xvbS7msmL+v/PoDrSJ8/Ryec8BwdoOe46+rq4Fk7QPcG9Dk6AAAA1wqCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMFbkQDcAYOBVbyge6BYAoF8QdADgGtKT0Omdu/oqdAKYga+uAACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBY/R50SktL5XK5VFhY6IzZtq3ly5fL4/Fo0KBBmjRpkg4ePBiyXVtbmxYuXKikpCTFxsYqNzdXx48fD6kJBALy+XyyLEuWZcnn8+n06dP9fUgAACBM9GvQ2bdvn9avX69vf/vbIeOrVq3SmjVrVFZWpn379sntdmvatGlqaWlxagoLC1VRUaHy8nLt2rVLZ86cUU5Ojjo7O52avLw81dXVqbKyUpWVlaqrq5PP5+vPQwIAAGGk34LOmTNn9OCDD+r555/XkCFDnHHbtvX000/rZz/7mX7wgx8oPT1dL774or744gu9/PLLkqRgMKgNGzboqaee0tSpUzV27Fi99NJL+uCDD/TWW29Jkg4fPqzKykr9y7/8i7xer7xer55//nn9+te/1ocffthfhwUAAMJIvwWdhx9+WPfdd5+mTp0aMl5fXy+/36/s7GxnLCYmRhMnTtTu3bslSbW1tero6Aip8Xg8Sk9Pd2qqq6tlWZYyMzOdmqysLFmW5dRcrK2tTc3NzSELAAAwV2R/7LS8vFzvvfee9u3b12Wd3++XJKWkpISMp6Sk6OjRo05NdHR0yEzQhZoL2/v9fiUnJ3fZf3JyslNzsdLSUq1YsaL3BwQAAMJSn8/oNDQ06O/+7u/00ksv6YYbbvjKOpfLFfLatu0uYxe7uOZS9Zfbz7JlyxQMBp2loaHhsu8HAADCW58HndraWjU1NSkjI0ORkZGKjIzUjh079E//9E+KjIx0ZnIunnVpampy1rndbrW3tysQCFy25uTJk13e/9SpU11miy6IiYlRfHx8yAIAAMzV50FnypQp+uCDD1RXV+cs48aN04MPPqi6ujrdcsstcrvdqqqqcrZpb2/Xjh07NH78eElSRkaGoqKiQmoaGxt14MABp8br9SoYDGrv3r1OzZ49exQMBp0aAABwfevza3Ti4uKUnp4eMhYbG6vExERnvLCwUCUlJRo5cqRGjhypkpISDR48WHl5eZIky7I0d+5cFRUVKTExUQkJCSouLtaYMWOci5tHjRqlGTNmKD8/X+vWrZMkzZs3Tzk5OUpLS+vrwwIAAGGoXy5G7s7ixYvV2tqqBQsWKBAIKDMzU9u2bVNcXJxTs3btWkVGRmrWrFlqbW3VlClTtHHjRkVERDg1mzdvVkFBgXN3Vm5ursrKyq768QDXtO2lA90BAAwYl23b9kA3MVCam5tlWZaCwSDX68BcPQg61Z98dhUaQV/xzl090C0AA6o3f78HZEYHwNVDiDFP9YbibmsIQ8B5/KgnAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFiRA90AgCu3tuqjbmuyrkIfAHCtIugAYSzr2PqBbgEArmkEHQAwUE9m+x6d9q2r0AkwsAg6AGCgns32re73PoCBxsXIAADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWv3UFXKu2lw50BwAQ9gg6wDWq+pPPBroFGK56Q3G3Nd65/PAnwhtfXQEAAGMRdAAAgLEIOgAAwFgEHQAAYKw+DzqlpaX67ne/q7i4OCUnJ+v+++/Xhx9+GFJj27aWL18uj8ejQYMGadKkSTp48GBITVtbmxYuXKikpCTFxsYqNzdXx48fD6kJBALy+XyyLEuWZcnn8+n06dN9fUgAACBM9XnQ2bFjhx5++GHV1NSoqqpKX375pbKzs3X27FmnZtWqVVqzZo3Kysq0b98+ud1uTZs2TS0tLU5NYWGhKioqVF5erl27dunMmTPKyclRZ2enU5OXl6e6ujpVVlaqsrJSdXV18vl8fX1IAAAgTLls27b78w1OnTql5ORk7dixQ/fcc49s25bH41FhYaGWLFki6fzsTUpKilauXKn58+crGAxq6NCh2rRpk2bPni1JOnHihFJTU7V161ZNnz5dhw8f1ujRo1VTU6PMzExJUk1Njbxer44cOaK0tLRue2tubpZlWQoGg4qPj++/kwBcgZ7c+gv0N24vx7WoN3+/+/0anWAwKElKSEiQJNXX18vv9ys7O9upiYmJ0cSJE7V7925JUm1trTo6OkJqPB6P0tPTnZrq6mpZluWEHEnKysqSZVlODQAAuL716wMDbdvWokWLdNdddyk9PV2S5Pf7JUkpKSkhtSkpKTp69KhTEx0drSFDhnSpubC93+9XcnJyl/dMTk52ai7W1tamtrY253Vzc/MVHhkAAAgH/Tqj88gjj+j999/Xv/7rv3ZZ53K5Ql7btt1l7GIX11yq/nL7KS0tdS5ctixLqampPTkMAAAQpvptRmfhwoV6/fXXtXPnTn3zm990xt1ut6TzMzLDhg1zxpuampxZHrfbrfb2dgUCgZBZnaamJo0fP96pOXnyZJf3PXXqVJfZoguWLVumRYsWOa+bm5sJOxgQXH8DAFdHnwcd27a1cOFCVVRU6O2339aIESNC1o8YMUJut1tVVVUaO3asJKm9vV07duzQypUrJUkZGRmKiopSVVWVZs2aJUlqbGzUgQMHtGrVKkmS1+tVMBjU3r17deedd0qS9uzZo2Aw6IShi8XExCgmJqavDxkAjMXvYSHc9XnQefjhh/Xyyy/r3//93xUXF+dcL2NZlgYNGiSXy6XCwkKVlJRo5MiRGjlypEpKSjR48GDl5eU5tXPnzlVRUZESExOVkJCg4uJijRkzRlOnTpUkjRo1SjNmzFB+fr7WrVsnSZo3b55ycnJ6dMcVAAAwX58HnWeffVaSNGnSpJDxF154QQ899JAkafHixWptbdWCBQsUCASUmZmpbdu2KS4uzqlfu3atIiMjNWvWLLW2tmrKlCnauHGjIiIinJrNmzeroKDAuTsrNzdXZWVlfX1IAAAgTPX7c3SuZTxHBwOFa3RgEr66wtV2TT1HBwAAYKAQdAAAgLEIOgAAwFgEHQAAYCyCDgAAMFa//tYVcD1aW/VRtzVZV6EPAABBB+hzWcfWD3QLAIA/IOgAAL4WfiYC1zKu0QEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIvn6AC90JPnhQAArh3M6AAAAGMxowMA6Hc9+Q24R6d96yp0gusNQQcA0O969htw/EwE+h5fXQEAAGMRdAAAgLEIOgAAwFgEHQAAYCwuRgb+oCd3hWRdhT4AAH2HoAP8Qc/uCgEAhBO+ugIAAMZiRgcAcE3oyU+seOfyrB30DjM6AADAWAQdAABgLL66wnWBO6oA4PpE0MF1gTuqADPw46DoLYIOACBs8OOg6C2u0QEAAMZiRgdhj+tvAABfhaCDsMf1NwCAr0LQAQAYhQcP4o8RdHBN68l/sACgtwhD1w8uRgYAAMYK+xmdZ555Rk8++aQaGxt122236emnn9bdd9890G2hB7iIGADQ38I66LzyyisqLCzUM888owkTJmjdunW69957dejQIQ0fPnyg20M3uIgYwLWMr7fM4LJt2x7oJq5UZmam7rjjDj377LPO2KhRo3T//fertLS02+2bm5tlWZaCwaDi4+P7s9XrDtfWAMB5hKG+15u/32E7o9Pe3q7a2lotXbo0ZDw7O1u7d+++5DZtbW1qa2tzXgeDQUnnTxh6bu//+9lAtwAAYeOtsoXd1tz54yeuQifmuPB3uydzNWEbdD799FN1dnYqJSUlZDwlJUV+v/+S25SWlmrFihVdxlNTU/ulRwAAemRh2UB3EJZaWlpkWdZla8I26FzgcrlCXtu23WXsgmXLlmnRokXO63Pnzunzzz9XYmLiV25jkubmZqWmpqqhoYGv6q4izvvA4LwPDM77wLjezrtt22ppaZHH4+m2NmyDTlJSkiIiIrrM3jQ1NXWZ5bkgJiZGMTExIWM33nhjf7V4zYqPj78u/kW41nDeBwbnfWBw3gfG9XTeu5vJuSBsn6MTHR2tjIwMVVVVhYxXVVVp/PjxA9QVAAC4loTtjI4kLVq0SD6fT+PGjZPX69X69et17Ngx/eQnPxno1gAAwDUgrIPO7Nmz9dlnn+nxxx9XY2Oj0tPTtXXrVt10000D3do1KSYmRr/4xS+6fH2H/sV5Hxic94HBeR8YnPevFtbP0QEAALicsL1GBwAAoDsEHQAAYCyCDgAAMBZBBwAAGIugY7gnnnhC48eP1+DBg3v8cETbtrV8+XJ5PB4NGjRIkyZN0sGDB/u3UcMEAgH5fD5ZliXLsuTz+XT69OnLbvPQQw/J5XKFLFlZWVen4TD1zDPPaMSIEbrhhhuUkZGhd95557L1O3bsUEZGhm644Qbdcssteu65565Sp2bpzXl/++23u3yuXS6Xjhw5chU7Dm87d+7UzJkz5fF45HK59Nprr3W7DZ/1/0PQMVx7e7t++MMf6m//9m97vM2qVau0Zs0alZWVad++fXK73Zo2bZpaWlr6sVOz5OXlqa6uTpWVlaqsrFRdXZ18Pl+3282YMUONjY3OsnXr1qvQbXh65ZVXVFhYqJ/97Gfav3+/7r77bt177706duzYJevr6+v1ve99T3fffbf279+vn/70pyooKNCWLVuucufhrbfn/YIPP/ww5LM9cuTIq9Rx+Dt79qxuv/12lZX17Pew+KxfxMZ14YUXXrAty+q27ty5c7bb7bZ/+ctfOmO///3vbcuy7Oeee64fOzTHoUOHbEl2TU2NM1ZdXW1Lso8cOfKV282ZM8f+/ve/fxU6NMOdd95p/+QnPwkZu/XWW+2lS5desn7x4sX2rbfeGjI2f/58Oysrq996NFFvz/v27dttSXYgELgK3ZlPkl1RUXHZGj7roZjRQYj6+nr5/X5lZ2c7YzExMZo4caJ27949gJ2Fj+rqalmWpczMTGcsKytLlmV1ew7ffvttJScn61vf+pby8/PV1NTU3+2Gpfb2dtXW1oZ8TiUpOzv7K89xdXV1l/rp06fr3XffVUdHR7/1apIrOe8XjB07VsOGDdOUKVO0ffv2/mzzusdnPRRBByEu/EjqxT+MmpKS0uUHVHFpfr9fycnJXcaTk5Mvew7vvfdebd68Wf/1X/+lp556Svv27dOf//mfq62trT/bDUuffvqpOjs7e/U59fv9l6z/8ssv9emnn/Zbrya5kvM+bNgwrV+/Xlu2bNGrr76qtLQ0TZkyRTt37rwaLV+X+KyHCuufgLheLV++XCtWrLhszb59+zRu3Lgrfg+XyxXy2rbtLmPXm56ed6nr+ZO6P4ezZ892/jk9PV3jxo3TTTfdpDfeeEM/+MEPrrBrs/X2c3qp+kuN4/J6c97T0tKUlpbmvPZ6vWpoaNDq1at1zz339Guf1zM+6/+HoBOGHnnkET3wwAOXrbn55puvaN9ut1vS+f8jGDZsmDPe1NTU5f8Qrjc9Pe/vv/++Tp482WXdqVOnenUOhw0bpptuukm//e1ve92r6ZKSkhQREdFlFuFyn1O3233J+sjISCUmJvZbrya5kvN+KVlZWXrppZf6uj38AZ/1UASdMJSUlKSkpKR+2feIESPkdrtVVVWlsWPHSjr/vfyOHTu0cuXKfnnPcNHT8+71ehUMBrV3717deeedkqQ9e/YoGAxq/PjxPX6/zz77TA0NDSGBE+dFR0crIyNDVVVV+ou/+AtnvKqqSt///vcvuY3X69V//Md/hIxt27ZN48aNU1RUVL/2a4orOe+Xsn//fj7X/YjP+kUG8kpo9L+jR4/a+/fvt1esWGH/yZ/8ib1//357//79dktLi1OTlpZmv/rqq87rX/7yl7ZlWfarr75qf/DBB/aPfvQje9iwYXZzc/NAHEJYmjFjhv3tb3/brq6utqurq+0xY8bYOTk5ITV/fN5bWlrsoqIie/fu3XZ9fb29fft22+v12n/6p3/Kef8K5eXldlRUlL1hwwb70KFDdmFhoR0bG2v/7//+r23btr106VLb5/M59Z988ok9ePBg+9FHH7UPHTpkb9iwwY6KirL/7d/+baAOISz19ryvXbvWrqiosD/66CP7wIED9tKlS21J9pYtWwbqEMJOS0uL899uSfaaNWvs/fv320ePHrVtm896dwg6hpszZ44tqcuyfft2p0aS/cILLzivz507Z//iF7+w3W63HRMTY99zzz32Bx98cPWbD2OfffaZ/eCDD9pxcXF2XFyc/eCDD3a5vfaPz/sXX3xhZ2dn20OHDrWjoqLs4cOH23PmzLGPHTt29ZsPI//8z/9s33TTTXZ0dLR9xx132Dt27HDWzZkzx544cWJI/dtvv22PHTvWjo6Otm+++Wb72Wefvcodm6E3533lypX2n/3Zn9k33HCDPWTIEPuuu+6y33jjjQHoOnxduEX/4mXOnDm2bfNZ747Ltv9whRIAAIBhuL0cAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGP9fziOaYWl3fa4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.figure()\n",
    "plt.hist(a8[\"BX\"], bins=50,\n",
    "        range=[-1.2,1.2], alpha=0.5)\n",
    "plt.hist(a8[\"tx_ref\"], bins=50,\n",
    "        range=[-1.2,1.2], alpha=0.5)\n",
    "\n",
    "print(np.mean(a8[\"BX\"] - a8[\"tx_ref\"]))\n",
    "print(np.std(a8[\"BX\"] - a8[\"tx_ref\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.636716336624133e-05\n",
      "0.006898310643139335\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.figure()\n",
    "plt.hist(a9[\"BX\"], bins=50,\n",
    "        range=[-1.2,1.2], alpha=0.5)\n",
    "plt.hist(a9[\"tx_ref\"], bins=50,\n",
    "        range=[-1.2,1.2], alpha=0.5)\n",
    "\n",
    "print(np.mean(a9[\"BX\"] - a9[\"tx_ref\"]))\n",
    "print(np.std(a9[\"BX\"] - a9[\"tx_ref\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.0000e+00, 1.8000e+01, 1.4000e+01, 6.0000e+00, 1.1000e+01,\n",
       "        1.4000e+01, 1.9000e+01, 2.3000e+01, 3.0000e+01, 2.8000e+01,\n",
       "        3.2000e+01, 4.6000e+01, 5.0000e+01, 6.3000e+01, 7.1000e+01,\n",
       "        7.7000e+01, 1.1300e+02, 1.1400e+02, 1.8700e+02, 2.1900e+02,\n",
       "        3.0900e+02, 4.7800e+02, 8.7900e+02, 2.6160e+03, 1.7745e+04,\n",
       "        3.7033e+04, 7.7140e+03, 2.2400e+03, 9.3700e+02, 5.3900e+02,\n",
       "        3.3700e+02, 2.6400e+02, 1.9500e+02, 1.3600e+02, 1.1700e+02,\n",
       "        1.1200e+02, 7.7000e+01, 6.0000e+01, 4.9000e+01, 4.3000e+01,\n",
       "        3.2000e+01, 2.0000e+01, 2.0000e+01, 2.0000e+01, 2.3000e+01,\n",
       "        2.0000e+01, 2.1000e+01, 1.6000e+01, 1.4000e+01, 1.4000e+01]),\n",
       " array([-0.25, -0.24, -0.23, -0.22, -0.21, -0.2 , -0.19, -0.18, -0.17,\n",
       "        -0.16, -0.15, -0.14, -0.13, -0.12, -0.11, -0.1 , -0.09, -0.08,\n",
       "        -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01,  0.  ,  0.01,\n",
       "         0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,  0.1 ,\n",
       "         0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,  0.19,\n",
       "         0.2 ,  0.21,  0.22,  0.23,  0.24,  0.25]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 27,
     "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 matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "a = a8\n",
    "a[\"slope_diff_rel\"] = (a[\"BX\"] - a[\"tx_ref\"]) / a[\"tx_ref\"]\n",
    "plt.figure()\n",
    "plt.hist(a[\"slope_diff_rel\", a[\"chi2_x\"] < 0.1], bins=50,\n",
    "        range=[-0.25,0.25], alpha=0.5, log=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 (conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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