Projektpraktikum/electron_lost_found.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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)]\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] \n",
    "\n",
    "#ak.num(tracked, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[[-676, -685, -694, -703, -764, -773, ..., -792, -854, -864, -864, -873, -882],\n",
       " [-1.4e+03, -1.45e+03, -1.5e+03, -1.54e+03, ..., -2.4e+03, -2.45e+03, -2.5e+03],\n",
       " [-148, -148, -149, -150, -156, -156, ..., -157, -158, -164, -164, -165, -166],\n",
       " [21.7, 23, 24.2, 25.4, 34, 35.3, 36.6, 37.9, 46.9, 48.2, 49.6, 50.9],\n",
       " [249, 250, 250, 250, 252, 252, 252, 252, 253, 253, 253, 254],\n",
       " [-134, -139, -145, -151, -191, -197, -203, -209, -251, -258, -264, -270],\n",
       " [213, 217, 220, 223, 244, 247, 250, 253, 275, 278, 282, 285],\n",
       " [1.17e+03, 1.19e+03, 1.21e+03, 1.23e+03, ..., 1.53e+03, 1.55e+03, 1.57e+03],\n",
       " [-135, -142, -149, -156, -207, -214, ..., -229, -229, -281, -289, -297, -304],\n",
       " [456, 460, 465, 469, 500, 504, 509, 514, 544, 549, 553, 558, 558],\n",
       " ...,\n",
       " [616, 629, 642, 655, 745, 758, 772, 785, 785, 878, 891, 905, 919],\n",
       " [-266, -267, -268, -269, -277, -277, ..., -279, -280, -287, -288, -289, -290],\n",
       " [-76.4, -73.2, -69.9, -66.6, -43.8, ..., -36.8, -33.3, -8.97, -5.34, -1.68],\n",
       " [-92, -90.5, -88.9, -87.3, -75.7, ..., -70.3, -57.5, -55.5, -53.6, -51.6],\n",
       " [-518, -527, -536, -545, -608, -617, -627, -636, -700, -710, -719, -729],\n",
       " [510, 517, 523, 576, 583, 583, 590, 597, 643, 650, 657],\n",
       " [1.2e+03, 1.21e+03, 1.21e+03, 1.22e+03, ..., 1.35e+03, 1.36e+03, 1.37e+03],\n",
       " [-1.33e+03, -1.33e+03, -1.34e+03, ..., -1.39e+03, -1.39e+03, -1.4e+03],\n",
       " [-1.27e+03, -1.29e+03, -1.32e+03, ..., -1.72e+03, -1.74e+03, -1.76e+03]]\n",
       "--------------------------------------------------------------------------------\n",
       "type: 9489 * var * float32</pre>"
      ],
      "text/plain": [
       "<Array [[-676, -685, -694, ..., -873, -882], ...] type='9489 * var * float32'>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "ax0.hist2d(ak.to_numpy(ak.flatten(l_sci_x)), ak.to_numpy(ak.flatten(l_pT)), bins=100,  cmap=plt.cm.jet)\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",
    "\n",
    "ax1.hist2d(ak.to_numpy(ak.flatten(f_sci_x)), ak.to_numpy(ak.flatten(f_pT)), bins=100,  cmap=plt.cm.jet)\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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S60+lwyPj1/XWghMlSbNrylSRP0qStPrJr0mSDmmEKlQsSVo35tL4sevD0n+b85Q4j/svM99oxpHO83BYzkZ7gW6+jym2zs1asVk/buaS3T/dXwNdleVDJhC6Bu+sAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr5ENBHS7dLk7XXFsVsq6zeFpNOMAtf5PfrKkHDPfZcYSZ/sZZnRyZoabcaiSmT5TnPFzZl4WHz7+ndc1WP9XkvRvDZYknadndIrZYa5e0pQ3/xnfOc8cWy7pZjOvdcqJx+yoxsTb7GpKVv25u+Pj3lulOpPbs+tIfJxxhvT0f8TnC0eZA/6n4lE5kmK/io+hfyiRE/S3kfMkSZO0W7foHknSAY1SlpolSRu3xi90YP+IGm41z4/NEHrHeU5szs9LkupsLo95clq8XqYY7XZOVBIf3qtRMt/H5jA1qmW+T+rPRF2atWq1li7/CfAL76wAAACv0awAAACv0awAAACv0awAAACv0awAAACv0awAAACv0awAAACv0awAAACv0awAAACvedGs/OIXv9CECRM0aNAgzZw5Uy+88ELQJQEAAE8E3qw8/vjjWr58uW677TZt375dZ511lhYuXKjy8vKgSwMAAB4IxWKxWJAFzJ49WzNmzNADDzyQWDvppJN0ySWXqLS09JjHRyIR5eXlSbpF0qDMFQp0uXQ5LE1qnenjrtkcnzols2RGOMfbYyJKhNxounPMv+PTMWOTJRSYzV8x41BJZvPC8/9LkvT0u5fqLx+7QJK0UfMlSTfqXr2lj0uSPrv1xfgBlZLqzXlseTdK+rBlqU9vkRYWJdc27ohPbc7PuUqm2JxhjqmplfKvMYs2L+g9SXea+cfiw5HcfspqPipJWjo4/ufKLG3TXbpdknSW4u/cbjw6T4f+cUL8oH+Yc3wo6WUz32/GLUpmIFVElMxJ2mvGOmdu83sKnRO4mT9yjpHir2ujM5ekqFrLdo45Hp3JoQIy7UNJ96i2tla5ubnt7hnoOysNDQ167bXXtGDBghbrCxYs0ObNm9MeU19fr0gk0uIGAAB6r0CblYMHD6q5uVmFhYUt1gsLC1VVVZX2mNLSUuXl5SVuxcXF3VEqAAAISOCfWZGkUCjU4n4sFmu1Zq1YsUK1tbWJW0VFRXeUCAAAApLuH827TUFBgfr379/qXZTq6upW77ZY4XBY4XC4O8oDAAAeCPSdlYEDB2rmzJnasGFDi/UNGzZo7ty5AVUFAAB8Eug7K5J0ww03aPHixZo1a5bmzJmjhx56SOXl5Vq6dGnQpQEAAA8E3qx84Qtf0KFDh3TXXXfpvffe09SpU/XUU09p/PjxQZcGAAA8EHizIklXX321rr766qDLAAAAHvLi20AAAABtoVkBAABeo1kBAABeo1kBAABeCzzIsLMIMkTwUsPi0n1ufUCa/SYpGU5nQ7walQy0m2dGe1+KBxSa840x6/8yS5+VZL/xv0vS8Ph07P+KB+jte2qSTjl/iyRpmuLJgSXaqyJVSpIW1/9WkjTkH0elQ+Y8R8z4J0mbUi4vX7qzLD69Y1R8rKmRXjeXN88EEP79UDzWT5JOMPvpWknPmPksSeeb+anxoWpknt7RJEnSWzpRklSmU7RWl8av5d2J8R1fDEnvm2PtL7POkbTGzEvMuE8yl2zuSNJgJS/UhgzWKPl81zgXbF+nAUoGFLqhkpb7M5D689AkggUBVw8JMgQAADgWmhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOC1dCEmQA9ns13q2t0rqaP/GQxw9o+a+Sglk29slkyhWZeSGTCNksm6Sa4VSrNMrTaD5xJJZ5r5UDM+K+kU+9Dx/IwR/2O/ivvFw3CKTSjOKFXrO/qZJGnYRe+ruKZakhR6KX7o4fOzNfw685x8xZzv/yh5+abkqmvyNHp6bfzOKrPth8kajxTH/x9nyB+O6o76+NoPvnWdJKlSRYmsoRqTt/O/dbvK1p8uSco+/bAkqe7Z4VKeOfdBSTeauc3vWahkhM8O+zrucor9mxndvB2b2ZOlRNbSrgNmLar4a5AqNaMnS8nX+Vg/P9F2tjV1YA6go3hnBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeI1mBQAAeC0Ui8ViQRfRGZFIRHl5eZJukTQo6HLQ5WzuS1uZKqm5Pk2SctIcY3NhCp37dSn7ZUnKN/MRZsxRMtPHBtfkSllntH6IaWacJWmbmZ9oxmZJw8z8XDPOq9dpRVvNo0TNodsSWT9v6OTEqedqsyRplOJZN5t1hi7VWknS++bE07RD/ZubJUm/679Y9Qqbh+4vSTpfTyUe5ymdL0n6sy7XNHNd1SYc6OP6f/qzLpMkvROJX3vdfwyXrjDF/LcZp0p628wfNeMwxSN84keZcauSeTs2t2m/Wmb11KilRiWzd3KcfdzXSjr2zwVZPIC/PpR0j2pra5Wbm9vunryzAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEaQITxnA+kGKBmMl+1sP8GMB8yYKyli5vlp1qabsU7J5EF77AApq6TlQ2RJOmzmNoCwRNJZZv6+ZHIHpQvjQ97pVWr4MB4iWPfBYEnSdUU/03OaJ0n6tF4whw7TQDVIkvorHkC4VadpugkWfLIhHjaYM/ADfVrPS5Ie2/p1SdLAyRE1bDPBX1Pr4+PesExmofSSkk9dlRk/kPSAmRebcZCz3eYGNu1SMvDRPhH/pWSgow0d3KXkc2yDCvcryW474KzZ/eqctSx1PKAQQO9BkCEAAOglaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXyAZCJ6TLcXGzfKzGlOOaJI0yc5szk69kXkyTkpk/NtMnO+Wcds1un+ysmXyabBPWU6dkxE2JGT9QMv7GROtomKQTU8p/X9JKM98WH/qdc0RHHx2SPGaY2f4PM4YlDTfzoWZ8U9IUM7dZQtmSNpq5/dGd4qwdNOMOJSWe8joln6+tZpwn6ZCZR9Ui80iStNc5xj5vTc5ajrNmH8g+cdXOWjofJcuHHCCgbyMbCAAA9BI0KwAAwGuBNislJSUKhUItbrfcckuQJQEAAM+094/Q3eKuu+7SVVddlbg/dOjQdvYGAAB9TeDNSk5OjkaPHh10GQAAwFOBf2ble9/7nkaMGKFTTjlFq1atUkNDQ7v719fXKxKJtLgBAIDeK9B3VpYtW6YZM2Zo+PDheuWVV7RixQrt2bNHv/71r9s8prS0VHfeeWc3VgkAAILU5e+srFy5stWHZlNv27bFf2HF9ddfr7PPPlvTp0/XN7/5Tf3yl7/Ub37zGx06dKjN869YsUK1tbWJW0VFRZv7AgCAnq/L31m59tprtWjRonb3KSkpSbt++umnS5J2796tESNGpN0nHA4rHA53qkYAANBzdHmzUlBQoIKCgo907Pbt2yVJY8aM6cqSAABADxbYZ1ZefvllbdmyRfPnz1deXp5effVVXX/99brooos0bty4oMoCAACeCaxZCYfDevzxx3XnnXeqvr5e48eP11VXXaWbbropqJL6OPdHob2sluw0a25mT13KthIlsnqUb8ZGSWNTjm1UMrjnBLXMDLLj9JTHOKREJpCNtYlGpFNnxOdvmrUvOnP7Lfm9kprN3GbwhCW9ZOYm+kdHFI+dkqRn48NRDZFmmbUyxWN4JOdpe1sabuo6bHN7TnB2sPXvUpINKoo42+1zc0CJ56Mp39kv19meej73dUj3eman2S+aZj93raszfMgEAtAxgTUrM2bM0JYtW4J6eAAA0EME/ntWAAAA2kOzAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvBZYNhAyyb6sTSlr6YLjUvfNUTK8Ll1ood0/V63DBk9QPCHQ1Shpkpnb/ac7x1hnSHrdOZ8N8bM1lCgeXCglw/cmJw+PRsxkl7R9tpmbWv5Q55zH1j9CyQBAs/bebkkpx+qAtMvWfZoZn5Wesw+c65zHPka1dLgxZa1JrZ/rRuc6dzvXli5k0LKP1aTWwYPu/bZebyvduQHAT7yzAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEazAgAAvEY2kLfS5fu0Z5SS2TsumzPTqHjuj5TMkHFzgEaZsVrxjB97jMx5C9Oc157P1pou1ybXOTbbGfeauc0I2uWc+80067uUvD5b335nu83Yseuuvc7cnrdJUiRlrVrJjB67zc3Qsdvc/2z+6czTvWap+T2p0r2+7eX2dPTnoaP7AYD/eGcFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWYFAAB4jWygbtPRrJ/UbJ229rHnizpjk7Pdjnb7eCUzc2ymj7vvgJT7knSSGV9SMifI1pWV5tg6JbNybH7PAXNz7VcyeyfXjI1qmSdkM3+ynDV7HjtmK/3zZLenPh/2sZWyPdrG9ra09dqQxwMAmcA7KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGs0KwAAwGsEGXa5LKUPtEtdy2pjW13Kdnebe4wbvme32e02dDDf2W+/koF+Tc5+NoSwxow5zny3Gd3AQPdx65zt9rx2bs/hPka6AEB7vmy1Dkt0HVDr5zD1OWhLe6GQx4OgQgAIAu+sAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr9GsAAAAr2W0WVm1apXmzp2rwYMHa9iwYWn3KS8v14UXXqghQ4aooKBA3/nOd9TQ0JDJsgAAQA+S0d9g29DQoCuuuEJz5szRb37zm1bbm5ubdcEFF2jkyJF68cUXdejQIS1ZskSxWEz33XdfJksDAAA9REablTvvvFOS9PDDD6fdvn79er3xxhuqqKhQUVGRJOmHP/yhrrzySq1atUq5ubmZLA8AAPQAgWYDvfzyy5o6dWqiUZGk8847T/X19Xrttdc0f/78VsfU19ervr4+cT8SiXRLrS2l5vZ09mm0xw9w1hqdeXbKWqOzZnNvmtQ6g8fNA3KPqWnnmEYlr2u/U197uTzHyujpSKZOW/k95PEAQF8X6Adsq6qqVFhY2GJt+PDhGjhwoKqqqtIeU1paqry8vMStuLi4O0oFAAABOe5mZeXKlQqFQu3etm3b1uHzhUKhVmuxWCztuiStWLFCtbW1iVtFRcXxXgIAAOhBjvvfL6699lotWrSo3X1KSko6dK7Ro0dr69atLdYOHz6sxsbGVu+4WOFwWOFwuEPnBwAAPd9xNysFBQUqKCjokgefM2eOVq1apffee09jxoyRFP/QbTgc1syZM7vkMQAAQM+W0Q/YlpeXq6amRuXl5WpublZZWZkkadKkSRo6dKgWLFigk08+WYsXL9YPfvAD1dTU6MYbb9RVV13FN4EAAICkDDcrt99+ux555JHE/VNPPVWS9Nxzz2nevHnq37+/nnzySV199dU644wzlJ2drS996Uu69957M1kWAADoQUKxWCwWdBGdEYlElJeXJ+kWSYO66VGP9dXljnzdNl2fmO6ry01q/dVld1/7ld9sZ5v7NWD32Hwzd7+6nPoV6HRfU063BgBAZ3wo6R7V1tYe819TyAYCAABeo1kBAABeo1kBAABeo1kBAABeCzQbKDid/cBo6rHHOpf7NDe1MU+97x6TmpuTlWatTulfTveDutVptrsfrE1XU1trAAB0D95ZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXqNZAQAAXuujQYbdHcx3rHDAdCGC7dXY1rbOhBASVggA8BPvrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/RrAAAAK/1ombleGKOslLmQUckNYlsHgAA0utFzQoAAOiNaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXaFYAAIDXgv49850Wi8XMrF4d/5X1Wc6+9ing190DANB96iW5f4+3rcc3K9Fo1Mx+EGgdAADg+EWjUeXl5bW7TyjWkZbGY0ePHlVlZaVycnIUCoWOuX8kElFxcbEqKiqUm5vbDRUGpy9dq8T19mZ96VqlvnW9felaJa7XFYvFFI1GVVRUpH792v9USo9/Z6Vfv34aO3bscR+Xm5vbJ35QpL51rRLX25v1pWuV+tb19qVrlbhe61jvqFh8wBYAAHiNZgUAAHitzzUr4XBYd9xxh8LhcNClZFxfulaJ6+3N+tK1Sn3revvStUpc70fV4z9gCwAAerc+984KAADoWWhWAACA12hWAACA12hWAACA12hWJNXX1+uUU05RKBRSWVlZ0OVkzEUXXaRx48Zp0KBBGjNmjBYvXqzKysqgy+pye/fu1Te+8Q1NmDBB2dnZmjhxou644w41NDQEXVrGrFq1SnPnztXgwYM1bNiwoMvpcr/4xS80YcIEDRo0SDNnztQLL7wQdEkZ8fzzz+vCCy9UUVGRQqGQ1q1bF3RJGVNaWqpPfepTysnJ0ahRo3TJJZforbfeCrqsjHnggQc0ffr0xC9HmzNnjp5++umgy+oWpaWlCoVCWr58+Uc+B82KpJtuuklFRUVBl5Fx8+fP1x//+Ee99dZb+stf/qJ33nlHl19+edBldbk333xTR48e1YMPPqidO3fqxz/+sX75y1/q1ltvDbq0jGloaNAVV1yhb3/720GX0uUef/xxLV++XLfddpu2b9+us846SwsXLlR5eXnQpXW5I0eO6JOf/KTuv//+oEvJuE2bNumaa67Rli1btGHDBjU1NWnBggU6cuRI0KVlxNixY3XPPfdo27Zt2rZtmz7zmc/o4osv1s6dO4MuLaNeffVVPfTQQ5o+fXrnThTr45566qnYlClTYjt37oxJim3fvj3okrrNE088EQuFQrGGhoagS8m473//+7EJEyYEXUbGrV69OpaXlxd0GV3qtNNOiy1durTF2pQpU2K33HJLQBV1D0mxtWvXBl1Gt6muro5Jim3atCnoUrrN8OHDY7/+9a+DLiNjotFobPLkybENGzbEzj777NiyZcs+8rn69DsrBw4c0FVXXaXf/e53Gjx4cNDldKuamho9+uijmjt3rgYMGBB0ORlXW1ur/Pz8oMvAcWpoaNBrr72mBQsWtFhfsGCBNm/eHFBVyITa2lpJ6hP/nTY3N2vNmjU6cuSI5syZE3Q5GXPNNdfoggsu0Lnnntvpc/XZZiUWi+nKK6/U0qVLNWvWrKDL6TY333yzhgwZohEjRqi8vFxPPPFE0CVl3DvvvKP77rtPS5cuDboUHKeDBw+qublZhYWFLdYLCwtVVVUVUFXoarFYTDfccIPOPPNMTZ06NehyMmbHjh0aOnSowuGwli5dqrVr1+rkk08OuqyMWLNmjf7+97+rtLS0S87X65qVlStXKhQKtXvbtm2b7rvvPkUiEa1YsSLokjulo9dr/ed//qe2b9+u9evXq3///vrqV7+qWA/5JcbHe62SVFlZqc997nO64oor9M1vfjOgyj+aj3K9vVUoFGpxPxaLtVpDz3Xttdfq9ddf1x/+8IegS8moE088UWVlZdqyZYu+/e1va8mSJXrjjTeCLqvLVVRUaNmyZfr973+vQYMGdck5e92v2z948KAOHjzY7j4lJSVatGiR/vrXv7b4A6+5uVn9+/fXl7/8ZT3yyCOZLrVLdPR60/3A7Nu3T8XFxdq8eXOPeCvyeK+1srJS8+fP1+zZs/Xwww+rX7+e1Zt/lNf24Ycf1vLly/X+++9nuLru0dDQoMGDB+tPf/qTLr300sT6smXLVFZWpk2bNgVYXWaFQiGtXbtWl1xySdClZNR1112ndevW6fnnn9eECROCLqdbnXvuuZo4caIefPDBoEvpUuvWrdOll16q/v37J9aam5sVCoXUr18/1dfXt9jWEVldXWTQCgoKVFBQcMz9fvazn+m73/1u4n5lZaXOO+88Pf7445o9e3YmS+xSHb3edGyfWl9f35UlZczxXOv+/fs1f/58zZw5U6tXr+5xjYrUude2txg4cKBmzpypDRs2tGhWNmzYoIsvvjjAytBZsVhM1113ndauXauNGzf2uUZFij8HPeXP3+NxzjnnaMeOHS3Wvva1r2nKlCm6+eabj7tRkXphs9JR48aNa3F/6NChkqSJEydq7NixQZSUUa+88opeeeUVnXnmmRo+fLjeffdd3X777Zo4cWKPeFfleFRWVmrevHkaN26c7r33Xv3rX/9KbBs9enSAlWVOeXm5ampqVF5erubm5sTvC5o0aVLiZ7unuuGGG7R48WLNmjVLc+bM0UMPPaTy8vJe+RmkDz74QLt3707c37Nnj8rKypSfn9/qz6ye7pprrtFjjz2mJ554Qjk5OYnPIOXl5Sk7Ozvg6rrerbfeqoULF6q4uFjRaFRr1qzRxo0b9be//S3o0rpcTk5Oq88e2c9KfuTPJHX6u0m9xJ49e3r1V5dff/312Pz582P5+fmxcDgcKykpiS1dujS2b9++oEvrcqtXr45JSnvrrZYsWZL2ep977rmgS+sSP//5z2Pjx4+PDRw4MDZjxoxe+/XW5557Lu3ruGTJkqBL63Jt/Te6evXqoEvLiK9//euJn+GRI0fGzjnnnNj69euDLqvbdPary73uMysAAKB36Xn/kA8AAPoUmhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOA1mhUAAOC1/w9iI+wgfolc0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(size=50000)\n",
    "y = x * 3 + np.random.normal(size=50000)\n",
    " \n",
    "# Big bins\n",
    "plt.hist2d(x, y, bins=(50, 50), cmap=plt.cm.jet)\n",
    "plt.show()\n",
    " \n",
    "# Small bins\n",
    "plt.hist2d(x, y, bins=(300, 300), cmap=plt.cm.jet)\n",
    "plt.show()\n",
    " \n",
    "# If you do not set the same values for X and Y, the bins won't be a square!\n",
    "plt.hist2d(x, y, bins=(300, 30), cmap=plt.cm.jet)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}