Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#look at particles only from Signal\n",
    "allcolumns = file.arrays()\n",
    "tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] \n",
    "\n",
    "#ak.num(tracked, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lost\n",
    "l_eph = lost[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(l_eph)\n",
    "l_pT = lost[\"pt\"]\n",
    "l_sci_x = lost[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(l_sci_x)\n",
    "\n",
    "#found\n",
    "f_eph = tracked[\"brem_photons_pe\"]\n",
    "ak.nan_to_num(f_eph)\n",
    "f_pT = tracked[\"pt\"]\n",
    "f_sci_x = tracked[\"scifi_hit_pos_x\"]\n",
    "ak.nan_to_num(f_sci_x)\n",
    "\n",
    "l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n",
    "f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n",
    "\n",
    "l_sci_x = ak.to_numpy(ak.flatten(l_sci_x))\n",
    "l_pT = ak.to_numpy(ak.flatten(l_pT))\n",
    "f_sci_x = ak.to_numpy(ak.flatten(f_sci_x))\n",
    "f_pT = ak.to_numpy(ak.flatten(f_pT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(l_sci_x, l_pT, bins=200,  cmap=plt.cm.jet, cmin=0) #, range=[[-3000,3000],[0,2000]])\n",
    "ax0.set_xlabel(\"scifi x\")\n",
    "ax0.set_ylabel(r\"$p_T$\")\n",
    "ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(f_sci_x, f_pT, bins=200,  cmap=plt.cm.jet, cmin=0) #, range=[[-3000,3000],[0,2000]])\n",
    "ax1.set_xlabel(\"scifi x\")\n",
    "ax1.set_ylabel(r\"$p_T$\")\n",
    "ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can see that the lost electrons cover a wider spread in the x direction of the scifi tracker, most widely scattered electrons have low pt\n",
    "\n",
    "D:\n",
    "heatmaps look fairly similar. lost e are more densely located between x \\in [1000,2000]. found e between x \\in [200,1500].\n",
    "we can see a near empty space around the x origin in both. lost seem to have less pt.\n",
    "\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.09e+04], [8.66e+03], [8.07e+04], ..., [5.63e+03], [6.29e+03], [2.26e+03]]\n",
      "[4.62e+04, 9.36e+03, 1.34e+05, 5.63e+04, ..., 2.01e+04, 6.94e+03, 7.83e+03]\n",
      "8657.132\n",
      "9355.866625028413\n"
     ]
    }
   ],
   "source": [
    "energy_found = tracked[\"energy\"]\n",
    "energy_found = energy_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_found)\n",
    "\n",
    "e_ph_found = tracked[\"brem_photons_pe\"]\n",
    "e_ph_found = e_ph_found[tracked[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_found, nan=[0])\n",
    "e_ph_found = ak.sum(e_ph_found, axis=-1, keepdims=True)\n",
    "print(e_ph_found)\n",
    "print(energy_found)\n",
    "\n",
    "energy_lost = lost[\"energy\"]\n",
    "energy_lost = energy_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(energy_lost)\n",
    "\n",
    "e_ph_lost = lost[\"brem_photons_pe\"]\n",
    "e_ph_lost = e_ph_lost[lost[\"brem_photons_pe_length\"]!=0]\n",
    "#ak.nan_to_num(e_ph_lost)\n",
    "e_ph_lost = ak.sum(e_ph_lost, axis=-1,keepdims=True)\n",
    "\n",
    "#e_ph_found, energy_found = ak.broadcast_arrays(e_ph_found, energy_found)\n",
    "#e_ph_lost, energy_lost = ak.broadcast_arrays(e_ph_lost, energy_lost)\n",
    "\n",
    "e_ph_found = ak.to_numpy(ak.flatten(e_ph_found))\n",
    "energy_found = ak.to_numpy(energy_found)\n",
    "\n",
    "e_ph_lost = ak.to_numpy(ak.flatten(e_ph_lost))\n",
    "energy_lost = ak.to_numpy(energy_lost)\n",
    "\n",
    "print(e_ph_found[1])\n",
    "print(energy_found[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_e_found = e_ph_found/energy_found\n",
    "q_e_lost = e_ph_lost/energy_lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q_e_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(q_e_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can clearly see that lost electrons are responsible for higher energy photons\n",
    "D:\n",
    "still able to see a trend that most electrons that give up all of their energy to photons are lost e. but nowhere near as extreme as for the B decay\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(e_ph_found/(1e3), energy_found/(1e3), density=False, bins=200,  cmap=plt.cm.jet, cmin=0, range=[[0,200],[0,200]])\n",
    "ax0.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
    "ax0.set_ylabel(r\"$E_e$ [GeV]\")\n",
    "ax0.set_title(\"found electron energy against photon energy\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(e_ph_lost/(1e3), energy_lost/(1e3), density=False, bins=200, cmap=plt.cm.jet, cmin=0, range= [[0,200],[0,200]]) #[[0,50],[0,50]])\n",
    "ax1.set_xlabel(r\"$E_\\gamma$ [GeV]\")\n",
    "ax1.set_ylabel(r\"$E_e$ [GeV]\")\n",
    "ax1.set_title(\"lost electron energy against photon energy\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "concentrated at the E_ph/E_0~1 line especially at lower energies.\n",
    "lost E_ph to E_0: fewer entries at lower q_e\n",
    "D:\n",
    "both energies are much smaller than in the B decay. otherwise similar pattern.\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "brem_vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "brem_vtx_x_found = brem_vtx_x_found[tracked[\"brem_vtx_x_length\"]!=0]\n",
    "brem_vtx_x_found = ak.to_numpy(ak.flatten(brem_vtx_x_found))\n",
    "\n",
    "brem_vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "brem_vtx_z_found = brem_vtx_z_found[tracked[\"brem_vtx_z_length\"]!=0]\n",
    "#print(ak.to_numpy(brem_vtx_z_found))\n",
    "brem_vtx_z_found = ak.to_numpy(ak.flatten(brem_vtx_z_found))\n",
    "\n",
    "brem_vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "brem_vtx_x_lost = brem_vtx_x_lost[lost[\"brem_vtx_x_length\"]!=0]\n",
    "brem_vtx_x_lost = ak.to_numpy(ak.flatten(brem_vtx_x_lost))\n",
    "\n",
    "brem_vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "brem_vtx_z_lost = brem_vtx_z_lost[lost[\"brem_vtx_z_length\"]!=0]\n",
    "brem_vtx_z_lost = ak.to_numpy(ak.flatten(brem_vtx_z_lost))\n",
    "\n",
    "#vtx_x_fit= ak.to_numpy(vtx_x_found)\n",
    "#vtx_z_fit = ak.to_numpy(vtx_z_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABkAAAAIlCAYAAACNejRdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzde3wU1f0//teSG4EmCwGTEIlKK1LlYi3WCNoPWC5KRWyxoqIUlAoWRVOgKOVTAT8CNbZIPyB8vFDiR0T89vcRK62lghcsRRRRWkFLbUUEJcRq3ICG3JjfH7tnd+d+2Zndmc3r+XjksdnZ2Zkzs7dz5pz3+4QkSZJARERERERERERERESURTplugBERERERERERERERERuYwcIERERERERERERERFlHXaAEBERERERERERERFR1mEHCBERERERERERERERZR12gBARERERERERERERUdZhBwgREREREREREREREWUddoAQEREREREREREREVHWYQcIERERERERERERERFlHXaAEBERERERERERERFR1mEHCBERERERERERERERZR12gBBR2k2ZMgW1tbWZLgYREREREVFgsV1FRERkjh0gRERERERERERERESUddgBQkSB89RTT6F///4oLCxEKBTCnj17Ml0kTQsXLkQoFLK83r///e80lCr4duzYgYULF+Lzzz+XLa+trUUoFMIHH3yQkXIREREREQWFl3Vnvfq6HraH7DE6v2wTERGpsQOEiNJi7Nix6NatG7p164b169djxowZ8fu/+MUvLG/nk08+waRJk/C1r30NmzdvxquvvoqzzjrLw5KT3+zYsQOLFi1SVfgvv/xyvPrqq+jVq1dmCkZERERE5DG32lVe0quvkzuMzi/bREREarmZLgARdQy///3v4/9PmTIFw4cPx5QpU2xv5x//+AdaW1txww03YNiwYS6WMJi+/PJLdOnSJdPFSAuzYz3llFNwyimnpLFERERERETp5Va7KluwPSTHNhERkRojQIjIse3bt2P06NEIh8Po3r07Lr/8crz33nue7W/KlCm4+OKLAQDXXHMNQqEQhg8fHi/LiBEjUFRUhC5dumDo0KH4wx/+oLmNM844Q7Vcma5K3N+3bx+uu+46hMNhlJWV4aabbkIkElE9/w9/+AO+8Y1voKCgAH369MEvf/lL28d36NAhjB8/HsXFxQiHw7jhhhvwySefqMr05ptv4gc/+AG6d++Or33ta/HH33vvPUycOBGlpaUoKCjA2WefjQcffFDzOP/2t7/h6quvRjgcRklJCWbNmoW2tjbs378fl112GYqKinDGGWegpqbGtNzPPPMMQqEQXnjhBdVjq1evju/PSTmVx7pw4UL89Kc/BQD06dMHoVAIoVAIL7/8sm6499///ndcd911KCsrQ0FBAU477TT88Ic/RHNzs60yffLJJ5g2bRoqKytRUFCAU045BRdddBG2bt1qeo6IiIiIiPSku11lVA6zNpVZndiovm4mHe2h5O241SbyS3sI0E6B5VZ7CGCbiIiCiREgROTIwoUL8V//9V+YMmUKqqur0dTUhEWLFmHEiBF455138JWvfEX3ubW1tY72+fOf/xwXXHABbr31VixZsgSXXHIJiouLsW3bNowaNQqDBg3CmjVrUFBQgFWrVuGKK67Ak08+iWuuucbhUQJXXXUVrrnmGkydOhVvv/025s2bBwD4zW9+E1/nhRdewJVXXokhQ4Zgw4YNaG9vR01NDY4ePWprX9///vcxYcIE3HLLLdi3bx9+/vOf45133sFrr72GvLy8+Hrjx4/Htddei1tuuQVffPEFAOCdd97B0KFDcdppp+FXv/oVysvL8ac//Qm33347/v3vf2PBggWyfU2YMAE33HADpk+fji1btqCmpgatra3YunUrZsyYgTlz5mD9+vW48847ceaZZ2L8+PG65R47dixKS0uxdu1ajBgxQvZYbW0tvvnNb2LQoEGOyqk81sGDB+Ozzz7DihUr8PTTT8dDu8855xzNPLd//etfcfHFF6Nnz56455570LdvXxw5cgTPPvssWlpaUFBQYLlMkyZNwptvvonFixfjrLPOwueff44333wTn376qcVXmIiIiIhILhPtKi1W21RmdeIf/ehHuvV1M+lsDwHutYn80h7S4mZ7CGCbiIgCSiIismnTpk0SAKmmpka2/B//+IcEQFq3bp3qOZdddpnUtWtXzb/Fixdb3vdLL70kAZB++9vfxpddeOGFUmlpqXTs2LH4sra2NmnAgAFS7969pZMnT8aXT548WTr99NNV212wYIGU/JUo7iuPccaMGVLnzp1l26yqqpIqKiqkpqam+LLGxkappKREsvI1K/b1k5/8RLb8iSeekJ1Psd7dd9+t2sall14q9e7dW4pEIrLlt912m9S5c2fps88+k23jV7/6lWy9b3zjGxIA6emnn44va21tlU455RRp/Pjxpscwa9YsqbCwUPr888/jy9555x0JgLRixQrH5dQ61vvvv18CIB04cEC2fO3atarl3/nOd6Ru3bpJ9fX1umW3WqavfOUrUnV1tfGJICIiIiKyKJPtKmXd2WqbykqdWK++ried7aHk7bjZJvJDe0iS1K+rm+0hSWKbiIiCiSmwiMi2u+++G1/72tdwxx13oK2tLf7Xp08fFBYW4v3331c9549//COOHz+u+fezn/3McVm++OILvPbaa/jBD34gGx2Vk5ODSZMm4fDhw9i/f7/j7Y8bN052f9CgQThx4gTq6+vj+9+1axfGjx+Pzp07x9crKirCFVdcYWtf119/vez+hAkTkJubi5deekm2/KqrrpLdP3HiBF544QV8//vfR5cuXWSvyXe/+12cOHECO3fulD1n7Nixsvtnn302QqEQxowZE1+Wm5uLM888EwcPHjQt+0033YSmpiY89dRT8WVr165FQUEBJk6c6LicymO148svv8S2bdswYcIE3Ty4dsp0wQUXoLa2Fvfeey927tyJ1tZWx2UjIiIiIvJLu8pOm8rLOnE620OAu22ijtAeAtgmIqJgYgcIEdlSV1eHt956C//6179QUFCAvLw82V9TUxO6deuWtvI0NDRAkqR46G+yiooKAEgpHLdHjx6y+wUFBQCApqam+P5PnjyJ8vJy1XO1lhlRrp+bm4sePXqoyq881k8//RRtbW1YsWKF6vX47ne/CwD497//LXtOSUmJ7H5+fj66dOki68QRy0+cOGFa9v79++Nb3/oW1q5dCwBob2/HunXrcOWVV8b35aScWq+rVQ0NDWhvb0fv3r1117FTpqeeegqTJ0/Go48+iiFDhqCkpAQ//OEPUVdX57iMRERERNQx+aldZadN5WWdOJ3tIcDdNlFHaA8BbBMRUTBxDhAisuXQoUMAgAceeCA+IblS8kR0XuvevTs6deqEI0eOqB77+OOPAQA9e/aML+vcubNssjdBq0Jsdf+hUEizwme3ElhXV4dTTz01fr+trQ2ffvqpqhMmebJ2UQYxOuvWW2/V3HafPn1slcWJG2+8ETNmzMC7776L999/H0eOHMGNN96YUjmVx2pHSUkJcnJycPjwYd117JSpZ8+eWL58OZYvX44PP/wQzz77LO666y7U19dj8+bNjstJRERERB2Pn9pVdtpUXtaJ2R7yd3sIYJuIiIKJHSBEZIsYvRIKhXD++ednuDRA165dUVVVhaeffhq//OUvUVhYCAA4efIk1q1bh969e+Oss86Kr3/GGWegvr4eR48eRVlZGQCgpaUFf/rTnxzv/4ILLsDTTz+N+++/Pz5a6NixY9i0aZOtbT3xxBMYPHhw/P7/+3//D21tbRg+fLjh87p06YJLLrkEb731FgYNGoT8/Hzbx+GG6667DrNmzUJtbS3ef/99nHrqqRg9erTr5VRG4egpLCzEsGHD8Nvf/haLFy+WdYSlWqbTTjsNt912G1544QX85S9/sX8QRERERNSh+aldZbdNJejVia3W15XYHrLGD+0hgG0iIgoOdoAQkS1f+9rXcMkll+A///M/cfz4cVRVVUGSJBw5cgQvvfQSJk+ebFpBddv
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(brem_vtx_z_found, brem_vtx_x_found, density=False, bins=300,  cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(brem_vtx_z_lost, brem_vtx_x_lost, density=False, bins=300, cmap=plt.cm.jet, cmin=1)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
    "B:\n",
    "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
    "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
    "D:\n",
    "lost brem vertices: we can very clearly see the concentration of vertices @ the beampipe\n",
    "both: less statistics in general, can still make out the tracking stations but not as well as in the B decay\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot singular tracks by fitting brem vertices\n",
    "vtx_z_found = tracked[\"brem_vtx_z\"]\n",
    "vtx_z_found = vtx_z_found[tracked[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_found = tracked[\"brem_vtx_x\"]\n",
    "vtx_x_found = vtx_x_found[tracked[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "vtx_z_lost = lost[\"brem_vtx_z\"]\n",
    "vtx_z_lost = vtx_z_lost[lost[\"brem_vtx_z_length\"]>3]\n",
    "\n",
    "vtx_x_lost = lost[\"brem_vtx_x\"]\n",
    "vtx_x_lost = vtx_x_lost[lost[\"brem_vtx_x_length\"]>3]\n",
    "\n",
    "def cubic_fit(x, a, b, c, d):\n",
    "    return (a + b*x + c*x**2 + d*x**3)\n",
    "\n",
    "def quint_fit(x, a, b, c, d, e, f):\n",
    "    return (a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/cetin/software/miniconda3/envs/env1/lib/python3.11/site-packages/scipy/optimize/_minpack_py.py:1010: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "n_end=100\n",
    "\n",
    "for i in range(0,n_end):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_found[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax0.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
    "    ax0.errorbar(ak.to_numpy(vtx_z_found[i,:]),ak.to_numpy(vtx_x_found[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax0.legend()\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks of brem vertices from few signals\")\n",
    "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(0,n_end):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]))\n",
    "    z_coord = np.linspace(vtx_z_lost[i,0],12000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax1.plot(z_coord, fit, \"-\", label=\"fit\"+str(i), lw=0.5)\n",
    "    ax1.errorbar(ak.to_numpy(vtx_z_lost[i,:]),ak.to_numpy(vtx_x_lost[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax1.legend()\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks of brem vertices from few signals\")\n",
    "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "ax1.grid()\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "we can see that of the lost brem vertices, many trajectory fits seem illogical and not plausible\n",
    "found: most seem like reasonable tracks\n",
    "D:\n",
    "both: many tracks arent good fits and are unusable\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "endvtx_x_found = tracked[\"all_endvtx_x\"]\n",
    "endvtx_x_found = endvtx_x_found[tracked[\"all_endvtx_x_length\"]!=0]\n",
    "endvtx_x_found = ak.to_numpy(ak.flatten(endvtx_x_found))\n",
    "\n",
    "endvtx_z_found = tracked[\"all_endvtx_z\"]\n",
    "endvtx_z_found = endvtx_z_found[tracked[\"all_endvtx_z_length\"]!=0]\n",
    "#print(ak.to_numpy(brem_vtx_z_found))\n",
    "endvtx_z_found = ak.to_numpy(ak.flatten(endvtx_z_found))\n",
    "\n",
    "endvtx_x_lost = lost[\"all_endvtx_x\"]\n",
    "endvtx_x_lost = endvtx_x_lost[lost[\"all_endvtx_x_length\"]!=0]\n",
    "endvtx_x_lost = ak.to_numpy(ak.flatten(endvtx_x_lost))\n",
    "\n",
    "endvtx_z_lost = lost[\"all_endvtx_z\"]\n",
    "endvtx_z_lost = endvtx_z_lost[lost[\"all_endvtx_z_length\"]!=0]\n",
    "endvtx_z_lost = ak.to_numpy(ak.flatten(endvtx_z_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(endvtx_z_found, endvtx_x_found, density=False, bins=500,  cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found end vertices\")\n",
    "ax0.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1 = ax1.hist2d(endvtx_z_lost, endvtx_x_lost, density=False, bins=500, cmap=plt.cm.jet, cmin=1)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"$e^\\pm$ lost end vertices\")\n",
    "ax1.set(xlim=(0,12000), ylim=(-4000,4000))\n",
    "\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "z: VeLo - RICH1 - TT - Magnet - T1,T2,T3 - RICH2 - M1\n",
    "B:\n",
    "vertices of lost e photons are more densely concentrated around the beampipe, especially in the z range of the magnet\n",
    "found: vertices are densely located @ or around the detectors, while there are no clusters in the z range of the magnet\n",
    "D:\n",
    "lost: densely located @ the beampipe.\n",
    "both: almost cant make out the velo or ut\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try to plot trajectories using all tracker hits (Velo, UT, SciFi)\n",
    "\n",
    "velo_x_found = tracked[\"velo_hit_pos_x\"]\n",
    "velo_z_found = tracked[\"velo_hit_pos_z\"]\n",
    "ut_x_found = tracked[\"ut_hit_pos_x\"]\n",
    "ut_z_found = tracked[\"ut_hit_pos_z\"]\n",
    "scifi_x_found = tracked[\"scifi_hit_pos_x\"]\n",
    "scifi_z_found = tracked[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tracker_x_found = ak.concatenate([velo_x_found,ut_x_found,scifi_x_found], axis=1)\n",
    "tracker_z_found = ak.concatenate([velo_z_found,ut_z_found,scifi_z_found], axis=1)\n",
    "\n",
    "velo_x_lost = lost[\"velo_hit_pos_x\"]\n",
    "velo_z_lost = lost[\"velo_hit_pos_z\"]\n",
    "ut_x_lost = lost[\"ut_hit_pos_x\"]\n",
    "ut_z_lost = lost[\"ut_hit_pos_z\"]\n",
    "scifi_x_lost = lost[\"scifi_hit_pos_x\"]\n",
    "scifi_z_lost = lost[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tracker_x_lost = ak.concatenate([velo_x_lost,ut_x_lost,scifi_x_lost], axis=1)\n",
    "tracker_z_lost = ak.concatenate([velo_z_lost,ut_z_lost,scifi_z_lost], axis=1)\n",
    "\n",
    "\n",
    "#tracker_x_found = tracker_x_found[tracked[\"energy\"]>1e4]\n",
    "#tracker_z_found = tracker_z_found[tracked[\"energy\"]>1e4]\n",
    "\n",
    "#tracker_x_lost = tracker_x_lost[lost[\"energy\"]>1e4]\n",
    "#tracker_z_lost = tracker_z_lost[lost[\"energy\"]>1e4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "nstart=0\n",
    "nend=130\n",
    "\n",
    "for i in range(nstart,nend):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]))\n",
    "    z_coord = np.linspace(tracker_z_found[i,0],14000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax0.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax0.errorbar(ak.to_numpy(tracker_z_found[i,:]),ak.to_numpy(tracker_x_found[i,:]),fmt=\".\",ms=3)\n",
    "\n",
    "ax0.legend([r\"$E>5$GeV\"])\n",
    "ax0.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax0.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax0.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
    "ax0.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks from detector hits from few signals\")\n",
    "ax0.set(xlim=(0,14000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(nstart,nend):\n",
    "    popt, pcov = curve_fit(cubic_fit,ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]))\n",
    "    z_coord = np.linspace(tracker_z_lost[i,0],14000,1000)\n",
    "    fit = cubic_fit(z_coord, popt[0], popt[1], popt[2], popt[3])\n",
    "    ax1.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax1.errorbar(ak.to_numpy(tracker_z_lost[i,:]),ak.to_numpy(tracker_x_lost[i,:]),fmt=\".\",ms=3)\n",
    "\n",
    "ax1.vlines(3000, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax1.vlines(7500, -4000,4000, lw=1, ls=\":\", color=\"red\")\n",
    "ax1.set_xticks(np.arange(0,14000,1000) , minor=True)\n",
    "ax1.set_yticks(np.arange(-4000,4000,500), minor=True)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks from detector hits from few signals\")\n",
    "ax1.set(xlim=(0,14000), ylim=(-4000,4000))\n",
    "ax1.grid()\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "electrons and photons will be stopped by the ECAL which serves to measure the particles energy\n",
    "\n",
    "B:\n",
    "the trajectories between the velo and tt should be linear, which cannot be plotted accurately using a single fit.\n",
    "lost tracks diverge more severely.\n",
    "\n",
    "most higher energy particles maintain a trajectory closer to the beamdirection ie a larger pseudorapidity,\n",
    "and show less bending in their trajectory, especially upstream.\n",
    "found: higher energy: very compact trajectory, less bending wrt lower energy particles \n",
    "\n",
    "D:\n",
    "E<10GeV: almost all diverge from the x origin (almost no hit for x<1500)\n",
    "E>10GeV: much more densely clustered. however still a noticeable empty space around the x origin\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 299792458 #m/s\n",
    "energy_found = tracked[\"energy\"]\n",
    "p_found = tracked[\"p\"]\n",
    "pt_found = tracked[\"pt\"]\n",
    "eta_found = tracked[\"eta\"]\n",
    "\n",
    "energy_lost = lost[\"energy\"]\n",
    "p_lost = lost[\"p\"]\n",
    "pt_lost = lost[\"pt\"]\n",
    "eta_lost = lost[\"eta\"]\n",
    "\n",
    "p_found = ak.to_numpy(p_found)\n",
    "pt_found = ak.to_numpy(pt_found)\n",
    "eta_found = ak.to_numpy(eta_found)\n",
    "\n",
    "p_lost = ak.to_numpy(p_lost)\n",
    "pt_lost = ak.to_numpy(pt_lost)\n",
    "eta_lost = ak.to_numpy(eta_lost)\n",
    "#print(np.sqrt(energy_found[0]**2 - p_found[0]**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(p_found, pt_found, bins=200, cmap=plt.cm.jet, cmin=0,range=[[0,1e5],[0,1e4]]) #range=[[0,1e4],[0,1e3]])\n",
    "ax0.set_xlabel(\"p\")\n",
    "ax0.set_ylabel(r\"$p_T$\")\n",
    "ax0.set_title(\"found electron momentum over transverse momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(p_lost, pt_lost, bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,1e5],[0,1e4]]) #range=[[0,1e4],[0,1e3]]) \n",
    "ax1.set_xlabel(\"p\")\n",
    "ax1.set_ylabel(r\"$p_T$\")\n",
    "ax1.set_title(\"lost electron momentum over transverse momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "\n",
    "D:\n",
    "both: clustered between 2000<p<6000 and 20<pt<400 (found a little more spread)\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(eta_found, p_found/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,2e2]]) #50]])\n",
    "ax0.set_xlabel(r\"$\\eta$\")\n",
    "ax0.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax0.set_title(\"found eta over electron momentum\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(eta_lost, p_lost/(1e3), bins=200, cmap=plt.cm.jet, cmin=0, range=[[0,7],[0,2e2]]) #50]])\n",
    "ax1.set_xlabel(r\"$\\eta$\")\n",
    "ax1.set_ylabel(r\"$p$ [GeV]\")\n",
    "ax1.set_title(\"lost eta over electron momentum\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "B:\n",
    "particles with lower momentum appear to have lower rapidity as well, ie a larger angle to the beam axis.\n",
    "D:\n",
    "both: clustered between 3<eta<5 and 0<p<10GeV. it seems that most particles had a higher rapidity \n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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     "execution_count": 16,
     "metadata": {},
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   ],
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    "tracked[1].tolist()"
   ]
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  {
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