makeJetImages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date: 12th June 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def partition_exp(n_particles=10, nevents=1, mu=1.):\n",
    "    evals = np.random.exponential(scale = mu, size=(nevents,n_particles))\n",
    "    sumRow =np.repeat(evals.sum(axis=1), n_particles).reshape(nevents, n_particles)\n",
    "    evals = np.sort(np.divide(evals,sumRow))[:, ::-1]\n",
    "    return evals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeJetImages(grid = 32, n_data = 10, n_particles = 5, isSignal = False):\n",
    "\n",
    "    '''inputs: image size, number of datapoints, number of particles in each subjet, signal or background'''\n",
    "    '''outputs: array of images, radii, theta, labels, summed image'''\n",
    "\n",
    "    # origin\n",
    "    center = int((grid-1)/2)\n",
    "    \n",
    "    # create input variables\n",
    "    if isSignal:\n",
    "        theta = np.random.normal(loc=0.2, scale=0.02, size=n_data) # signal_d\n",
    "        z = np.random.normal(loc=0.5, scale=0.02, size=n_data) #signal_z \n",
    "        labels = np.ones(shape=(n_data,1))\n",
    "    else:\n",
    "        theta = np.random.exponential(scale=0.02, size=n_data) # bkg_d\n",
    "        z = np.random.exponential(scale=0.02, size=n_data) #bkg_z\n",
    "        labels = np.zeros(shape=(n_data,1))\n",
    "    \n",
    "    # masking z to ensure z<1.0\n",
    "    z = np.where(z<1.0, z, z%1) #signal_z   \n",
    "    # making so that z is always more than 1-z\n",
    "    z = np.where(z>0.5, z, (1-z))\n",
    "    z = np.repeat(z, n_particles).reshape(n_data, n_particles)\n",
    "    # masking theta to ensure theta<0.5\n",
    "    theta = np.where(theta<0.5, theta, theta%0.5)\n",
    "    theta = np.repeat(theta, n_particles).reshape(n_data, n_particles)\n",
    "    \n",
    "    # distributing pT fractions between particles\n",
    "    particles1 = np.multiply(partition_exp(n_particles, n_data), z)\n",
    "    particles2 = np.multiply(partition_exp(n_particles, n_data), (1-z))\n",
    "    \n",
    "    # one jet has two subjets \n",
    "    radii = np.random.uniform(0.25,0.5 ,size = (n_data,2))\n",
    "    radii1 = np.array([np.multiply(np.ones(shape = (n_particles)),y) for y in radii[:,0]])\n",
    "    radii2 = np.array([np.multiply(np.ones(shape = (n_particles)),y) for y in radii[:,1]])\n",
    "    dtheta1 = np.random.exponential(scale=0.5, size=(n_data, n_particles)) # delta theta for first subjet\n",
    "    dtheta1= np.sort(np.where(dtheta1<radii1, dtheta1, dtheta1%radii1))\n",
    "    phi1 = np.random.uniform(0, 2*math.pi, size=(n_data, n_particles)) # angular\n",
    "    dtheta2 = np.random.exponential(scale=0.5, size=(n_data, n_particles)) # delta theta for second subjet\n",
    "    dtheta2= np.sort(np.where(dtheta2<radii2, dtheta2, dtheta2%radii2))\n",
    "    phi2 = np.random.uniform(0, 2*math.pi, size=(n_data, n_particles)) # angular\n",
    "    \n",
    "    # position of particles on the grid : subscripts 1, 2 for particles in jet1, jet2 respectively   \n",
    "    x1 = (np.multiply(dtheta1, np.cos(phi1))*grid).astype(int) + center\n",
    "    x2 = (np.multiply(dtheta2, np.cos(phi2))*grid).astype(int) + center\n",
    "    y1 = (np.multiply(dtheta1, np.sin(phi1))*grid).astype(int) + center\n",
    "    y2 = ((np.multiply(dtheta2, np.sin(phi2))-theta)*grid).astype(int) + center\n",
    "\n",
    "    ybins1 = np.arange(0,grid+1)\n",
    "    xbins1 = np.arange(0,grid+1)\n",
    "    \n",
    "    # H1, H2 are the images from the first and second subjet respectively  \n",
    "    H1_ = (np.vstack(np.array([(np.histogram2d( y1[i], x1[i], bins=(xbins1, ybins1), weights = particles1[i])) for i in range(n_data)])[:,0])).reshape(n_data, grid, grid)\n",
    "    H2_ = (np.vstack(np.array([(np.histogram2d( y2[i], x2[i], bins=(xbins1, ybins1), weights = particles2[i])) for i in range(n_data)])[:,0])).reshape(n_data, grid, grid)\n",
    "    H_ = (H1_+H2_) # images witb both subjets\n",
    "    # performing a parity flip so that the right side of the jet image has the highest sum pixel intensity\n",
    "    H_parity = np.sum(H_, axis=1)\n",
    "    if (grid%2==0):\n",
    "        H_left_ = np.repeat(np.sum(np.sum(H_, axis=1)[:,:center+1], axis=1), grid**2).reshape(n_data,grid,grid)\n",
    "    else:\n",
    "        H_left_ = np.repeat(np.sum(np.sum(H_, axis=1)[:,:center], axis=1), grid**2).reshape(n_data,grid,grid)\n",
    "    H_right_ = np.repeat(np.sum(np.sum(H_, axis=1)[:,center+1:], axis=1), grid**2).reshape(n_data,grid,grid)\n",
    "    images = np.where(H_right_>H_left_, H_, np.flip(H_, axis=2))\n",
    "    avgImages = np.sum(images, axis =0)\n",
    "    z = z.reshape(n_data*n_particles,1)[::n_particles]\n",
    "    theta = theta.reshape(n_data*n_particles,1)[::n_particles]\n",
    "\n",
    "    return images, radii, z, theta, labels, avgImages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = 32\n",
    "n_data = 10000\n",
    "n_particles = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 'Signal' jet images and their plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Time is  4.025918278843164 seconds for  10000  samples.\n",
      "(10000, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "tic = time.perf_counter()\n",
    "isSignal1 = True\n",
    "images1, radii1, z1, theta1, labels1, avgImages1 = makeJetImages(grid, n_data, n_particles, isSignal1)\n",
    "toc = time.perf_counter()\n",
    "print('Processing Time is ',toc-tic, 'seconds for ',n_data, ' samples.' )\n",
    "\n",
    "print(images1.shape)\n",
    "np.savez('data/jetImages_signal', jetImages = images1, radiilab = radii1, zlab = z1, thetalab = theta1, labels = labels1, sumedImage = avgImages1)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average signal image - log scale norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BDJsQogLHONiuMlUNQoZNCFFBLzxj6xzDNi6uX8DS4uZ9TAm7vJm1oSzzYmab1mewp7A986Rm3s3oepB7Pl+2M6lREKXkj52A6UZ3NiayqYlsbiILyDbqH5wXe7n7IxcsMDe4Idl3J6thkXlgM1l2zSdYWCx4f9ilqciwCSF6khri2DoSeUWFEBUMbamq5kBeUSFENyCvqBCi5yh5RaveK9qRyLAJISpQdg8hRE8ir2ijuCmRBXvWdz92RrEA2PGKON7gFclG5myDfJR3P6t5kG10z8JOTtyfhHQ8F4vC6JI46iQPBVmXyLJKstE3K/t7ScJHpu94MZQdmhvv8I9CMKbx+7DPPiaHsiy5Qt4vSZQwLrpmXAjE/jQQTH7DG+KBqkPhHkKInkOGTQjRc7RzS5WZTQL+J3AIuNvdb6vnOopjE0JUMDRjqzK7x6iY2Uoz22VmG0a0LzOzTWa22cxWlJvfDXzb3a8E3lnvZ5BhE0K8hEYaNuAWYNnwBjPrA24ELgAWAZeZ2SLgdP741Le+DK5oKSqEGEGNVapmmtlwF9Oguw9WXM99rZkNjOh3LrDZ3R8HMLNVwHJgGyXj9iBjmHjJsAkhKqgxju0w8AC1F3M5jUp//DbgPODLwFfM7B1A3cVhRtXezFYCFwK73P3sctt1wJXA7vJpn3D3NfUqAcBALFrwZ78ubM9CM96QxClkuelPSeIiFrIpuF6UUgPmPhMXFLC4Wx6ekWXjiGS/SfokIR3X/b9Eli0UFgftC5I+2WdOsoWc+tyzoWzGnGLZ1qmnhn1mJgUiTuKFUJaF9mxhXtzv5cX9Nr08yPoBPHFxNKOKM53UQru2VLn788Bfj/U61Uz1bmHE+rjMF919cfkYm1ETQnQMQ+X3qjmofxP8dmB4IOrp5baGMOqMLVgfCyF6lBZVgr8fWGBm8ygZtEuB99ZxnULG4hW9xsweKrtyg/phYGZXmdk6M1u3e/fu6DQhRIfQ6LRFZnY7cC+w0My2mdkV7n4EuAa4k1I60zvcPa6nWSP1Og++CnyaUm3VTwOfBz5QdGLZQzIIsGTJEq9zPCFEC2nkMzZ3vyxoXwM05TFWXTM2d9/p7kfd/RjwdUquWyFED1BjgG7vJJo0sznuPuRafBewITtfCNE9tOgZW1OpJtzjdkrlVGaa2TbgWmCpmS2mtBTdAnxozJp8Khb95muvK2x/46v/NeyTudfrzTUVZXbIinhsnR673//D9OLwEYDpv42zWaTb+KIMGVnhlUSWhnS8PZFFYR1nJn1OSWRJv+dPiRceO/uLL7o1ueAO5oSyp5kRyh5jfl39HuI1he1PbU9iXC6cWNz+i7Hv8Sx5RXu8/F6wPr65CboIITqAGrN7TDWzQWoP0G0q2nkghHgJqnkghOgpanzG1pEou4cQogKV3xNC9BxDW6qqREtRIUTn0wtL0c4xbLGnPNRyH1PCLvUWWKmH7EHrKewMZf0Hi4vDlITJgNlvLeo3KemTRBWk2Tiy8IwgGqHuz1UnUWhPVpQly+CR/cHPSLKC7GVaKIuyiTy15aywD9uC9uQrVQvdXn5Pz9iEEBUctzsPhBC9S41xbHrGJoToDvSMTQjRUxzjhN7fUiWEOP5QweRGsSSRBR6gvoXxDu2+Oit39Sc548cF18y+BHsJc3Ayc1zsReP5Y7Es+2caOfuy33QzvgXRLYmdkXGfUfodHRd3nBD8PrMZyQFOTBSJeSHplyVKCL8/M5PB3hq0x+U8qqYXKsHLKyqEqMApPWOr5kBeUSFEd1BT+T15RYUQnU8vLEVl2IQQFTjGwer3inYkMmxCiApqrATfkXS39kKIpqClaKP4P7Fo6gefKmzP8sifwdZQVq87/2AQIpCFlmThI31HkpCUbNP684ksGu7ZpE8m+10iO7sOPeqLwsnvR0Ir/0DrjdbfcbA4C8H4mc+FfQ5veFmxIN6/XzV6xiaE6Dkc4+ixzjJsZnYW8ElgqrtfPNr5imMTQlTgx4yDL/ZXdVSDma00s11mtmFE+zIz22Rmm81sRaqT++PufkW1n0EzNiFEBe7G0SMNnbHdAnwF+MZQg5n1ATcCb6O0t+h+M1tNaf/J9SP6f8Ddd9UyoAybEKISpxbDNtPM1g17P+jugxWXc19rZgMj+p0LbHb3xwHMbBWw3N2vBy6sS+9hyLAJISpwN44crtqw7XH3bKd3xGlQ4eHbBpwXnWxmM4DPAueY2d+XDWCIDJsQYgTGsaNVm4aWFEx296eBq6s9f1TtzWwlpanhLnc/u9w2HfgWMABsAS5x99/Xoe8feTEWHXyxOAp6Rn+c4D0LBVnEo/FYSdaHKHQjyiABeSjIvv64ZsOkg0kMRhbuEamfZdXYEYse3RDLFmX/p6PbX++jm/gWp+xidp0DFvNkUiAiy+QyO6l9Mbe/+Bew8Z7Xx4r8JBaNGQca+4ytiO3AGcPen15uawjVeEVvAZaNaFsB3OXuC4C7yu+FEL3AMYMXx1V3lDfB1zFbux9YYGbzzGwCcCmwulEfYVTD5u5rgWdGNC8Hbi2/vhW4qFEKCSE6gCNVHlWkLTKz24F7gYVmts3MrnD3I8A1wJ3ARuAOd3+kUerX+4xttrsPpbR7CuL5vpldBVwFcOaZZ9Y5nBCiZZQSslXLqGmL3P2yoH0NsKYm3apkzAG67u6UbkUkH3T3Je6+ZNasWWMdTgjRbIYMW4NmbO2g3hnbTjOb4+5PmtkcoKbgOSFEB+PA4arP7shEk/XO2FYDl5dfXw78oDHqCCHajlPyQldzdCjVhHvcDiylFGG8DbgWuAG4w8yuAJ4ALhmzJkmmiBe3TS9s33okVv+cGb8KZTs5JZRNYV8o20dxeMaUoB3gZPaGsjSDQpbNItuiF0WXZFk1ki/oE0m3RdkXOwrfyfok30ZP7sfBvviGTKA4JGhPEg60P/l9Tkt+n3uS6it7mRbKNm5fVCyYGHaB84P2B5M+1VLbM7aWxLHVyqiGLXrwB/xFg3URQnQCDXYetAPtPBBCVFKbYetIlLZICFHJcewVFUL0MlqKCiF6imOke7e7ARk2IUQlPfCMrXMM20Ai21LcPPfVcVqKRwlc6MDixCeeZQWJXP1ZEY8oRATgRA6EssNJSMf4ybEsDAWZmvQpjqYBIMkvAUE9ESDO7pGFsSThDVmyiXqK8xxKY2ZismwtfYk1ODGpsjJ5WnGI0f57khuyNGh/LO5SNcdDuIcQ4jhD4R5CiJ5ES1EhRE+hZ2xCiJ7jGA0pvNxOZNiEEJU4+d7iLkA7D4QQL0U7DxqDfySW2X3F7XsPxhkTTuqPQykyDnBSKDtEcVGZo8ltzIrDZP0OTYz/54yfdCyUhYlLktowJImNZ/9Z0u+sRBaFdWRhJ0koSHY/xmUFc5Jwm4gsNCP7fjydZPfYWlG3pJI5k4rDln4zOUnMuuK5QNCAqZa8okKInkPOAyFEz6EtVUKInkQzNiFET6GlqBCi56itmEtH0jGGzT6XCAPn57NbTg27nPie2CuabXSfzc5QFm12zzxvWQ2FyMsKsK8/vuaEl8UuzvHPB4KspGv2LVicyOYlssj7mXhFX0w24x/oj72RO+OytuE9Ppjc+y1JRobsu5Ntxs++I4/f9upiwaqwCywOMhBsSrIFVEsHxrGZ2UXAOyilXrjZ3X+ana84NiFEJbVl0B0VM1tpZrvMbMOI9mVmtsnMNpvZilQl9++7+5XA1cB7RhuzY2ZsQogOwWn0lqpbgK8A3xhqMLM+4EbgbcA24H4zWw30AdeP6P8Bdx+qXfypcr8UGTYhRCW1LUVnmtm6Ye8H3X2w4nLua81sYES/c4HN7v44gJmtApa7+/XAhSMHMTOjVPbzx+7+wGhKybAJISqpzSu6x92X1DHKacDWYe+3Aecl5/8N8FZKW7jmu/vXsovLsAkhKunADLru/mXgy9WeL8MmhKikNeEe26FiA+3p5baGMCbDZmZbgH2UVuRH6pySAuB/l4xzV3H79KX13Yc9ics+Cx2INkefHNRCgDw84GhSKyHNrT81ls08ur+w3bIU/1kdgmcS2YLar3k42qQP7JsUF3PYFe7uz2VREoK9UQwRsImFoeykpE5Fxq7nkw8efey/jbv48uJ2s/Xrq1Yqo/pnbPVugr8fWGBm8ygZtEuB99ZxnUIaEe7x5+6+eCxGTQjRQQztFa3mqCJtkZndDtwLLDSzbWZ2hbsfAa4B7gQ2Ane4+yON+ghaigohKqltKTrqjM3dLwva1wBratKtSsY6Y3Pgp2a23swKP5yZXWVm68xs3e7du8c4nBCi6QyFe1RzdChjNWznu/vrgQuAj5jZm0ee4O6D7r7E3ZfMmpUkzhNCdA7HcwZdd99e/rnLzL5HKehubSMUE0K0iR7IoFv3jM3MJpnZlKHXwNuBDXkvIUTH02DnQTsYy4xtNvC90k4HxgHfdPefNESrkby/uPmZG04Lu5z1X2IHy6GkDkEWZhHlrZ/G78M+GdOSMJEs7CQLEzkwvTjDxMlT47FetjN5UpyFdMyNRc9PLf6fubc/DrPIPnOWOSOrKxGF2+xIlH8hGSsLE8m+V9Mmxfd//6nBI5qbwi4QhHs0hB6YsdVt2Mp7vF7XQF2EEJ1ClyeaVNoiIUQlQ+Ee1Rw9uBQVQvQitWX36K2lqBCiR1HNAyFEz3GMRieabDkybEKIl9LBuwqqoTsM28VB+564y+O/DgpkAK973S9DWVZgJQrPeIG40MheTg5lEzgUyrKiMll4Q3TNPX1JEZK58b/nU5+JC8c8dUpcmSUqlvI0M8M+WShFFu6RF1gp/t1kY01OCvA8xGtCWcaOnUlszN1B+03xd4Cvx6ExDcGrPrMl+dhqpTsMmxCiU+lI54HCPYQQPYcMmxCi59BSVAgxgu53i2rGJoQYQU1bD7TzQAjRDdQUoduRzoOuMGz+xeJ2++uk0+JY9OvtsfDAaXFYwUL+vbhPEu6xkE2hLAtTyMJOsjCRKIwhK0KSZTSZsiAOfciKnvQFfxhZaEx2H7Nwjyzbyc6k0EtElMUF8gwe2T0+ti2pmPPKSJCEeySZUMZOa8pUNZOuMGxCiFYiwyaE6DmcbnceyLAJIUbQ/bvgZdiEECPQUlQI0XN03ozNzF4FfBSYCdzl7l/Nzu9uwzYxkd2SyD4Ti36zPfb0EZRYWML6sMtmXhHK5vNYKMs8hPuYEspOYVdh+5RkY3fGjP4400Dmud3BQGF75oHdx+RQliUTyDymuwLvYeaRjvpAfh83PLYklPHWWORRyQx/bdypqTR2xmZmK4ELgV3ufvaw9mXAl4A+4CZ3vyHUyH0jcLWZnQB8A+hhwyaEaAINn7HdAnyFkkECwMz6gBuBtwHbgPvNbDUlI3f9iP4fKJf4fCfwYeB/jzagDJsQYgQ1bamaaWbrhr0fdPfB4Se4+1ozGxjR71xgc7koFGa2Clju7tdTmt29BHdfDaw2sx8B38yUkmETQoygpqXoHndP1uAhpwFbh73fBpwXnWxmS4F3A/3AmtEuLsMmhCig6qVoSxJNuvvdxCk5X4IMmxBiBDXN2OrdK7odKvaunV5uawjK7iGEGEFLsnvcDywws3lmNgG4FFjdGP3HOGOrxV3bFFYlslMT2d44TmTq/KdCWbSB++csDfss4tFQdjS5/QP8NpRF9QQAfh9sgp8dhIFAHsLwGPNDWbYJPtqYnm1Y30FcF2BPEp4xLgkhia6ZhdNsfjYO0XnxnumhLIlWIYh+AcD+W3G7fzq5XlNpbHYPM7sdWErJ0bANuNbdbzaza4A7KdmPle7+SP06V1K3YYvcte4e/yULIbqAmryioz5jc/fLgvY1VOEIqIexzNgK3bWQTFGEEF1AS56xNZWxPGMrcte+JDbfzK4ys3Vmtm737t1jGE4I0RqGlqLVHMdpBt1ysN4gwJIlS6qvViiEaBPH94ytqe5aIUS7OL5nbH9w11IyaJcC722IVkKINlKT86AjZ2zmXv/q0Mz+E/A/+KO79rOjnL8beKL8diYQp45oHdKjEulRSbfp8XJ3nzWWgczsJ+XxqmGPuy8by3jNYEyGbUwDm62rc4+Z9JAe0kOkaOeBEKLnkGETQvQc7TRsg6Of0hKkRyXSoxLp0YW07RmbEO0es3EAAAMXSURBVEI0Cy1FhRA9hwybEKLnaIthM7NlZrbJzDab2Yp26FDWY4uZPWxmD47I297scVea2S4z2zCsbbqZ/czMflP+GZdlaq4e15nZ9vI9ebAcq9hsPc4ws5+b2aNm9oiZfbTc3tJ7kujR0ntiZhPN7N/M7NdlPf6h3D7PzO4r/918q5zHTBTh7i09KAXzPgacBUwAfg0sarUeZV22ADPbMO6bgdcDG4a1/SOwovx6BfC5NulxHfC3Lb4fc4DXl19PAf4dWNTqe5Lo0dJ7Ahgwufx6PHAf8CfAHcCl5favAR9u5e+pm452zNj+kO7I3Q9RShe5vA16tA13Xws8M6J5OXBr+fWtwEVt0qPluPuT7v5A+fU+YCOlTDEtvSeJHi3FS+wvvx1fPhx4C/DtcntLviPdSjsMW1XpjlqEAz81s/Vm1u79brPd/cny66cgqdrbfK4xs4fKS9WmL4mHUy7Tdg6lWUrb7skIPaDF98TM+szsQWAX8DNKq5y97j6U2radfzcdz/HuPDjf3V8PXAB8xMze3G6FoPQfm5LRbQdfBV4BLAaeBD7fqoHNbDLwHeBj7v7ccFkr70mBHi2/J+5+1N0XU8qacy7wymaP2Uu0w7B1TLojd99e/rkL+B6lL1C72GlmcwDKP+MiBU3E3XeW/6iOAV+nRffEzMZTMia3uft3y80tvydFerTrnpTH3gv8HHgjMM3MhjLyKE1YQjsMW1Or01SLmU0ysylDr4G3AxvyXk1lNXB5+fXlwA/aocSQISnzLlpwT8zMgJuBje7+hWGilt6TSI9W3xMzm2Vm08qvT6RUV2QjJQN3cfm0tn1HuoG27DyoNd1Rk3Q4i9IsDUp56b7ZKj2GV+0BdgLXAt+n5PU6k1Jqp0vcvakP9gM9llJacjklr/GHhj3napYe5wO/AB6mlAwM4BOUnm+17J4kelxGC++Jmb2WknOgj9Lk4w53/+/l7+wqYDrwK+Cv3P1gs/ToZrSlSgjRcxzvzgMhRA8iwyaE6Dlk2IQQPYcMmxCi55BhE0L0HDJsQoieQ4ZNCNFz/H9lx3nxEERIfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im1 = plt.imshow(avgImages1, interpolation='nearest', origin='low', norm=LogNorm(), cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Individual 'signal' jet images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B6O7ujiVLlsR73vOe2Lx5c+zZs6eRtUHd5JNcySY5k09yJZs0Q92N0MjISFx55ZUX14sXL46RkZEx5w0MDMSaNWtizZo1EfHTei8H0zKVfMom7WDvJGf2TnJl76QZmv6whK1bt8bQ0FAMDQ1FxKXNvhxMmWySM/kkV7JJzuST6ah7RqirqyuOHj16cT08PBxdXV0TvuaKK87EVVc9GcePH48PfOAD9V66JXKvMff6Isav8ciRM02/9nTzKZuNNZtrPHLkSFOva+9sr9zri7B3NkteNT5Z82heNY41UX32zvrlXl/E7K5xwr0z1ens2bPpN37jN9IPf/jD9LOf/Sx9+MMfTi+88MKUXrt69ep6L9syudeYe30ptbfGevPpv2tjqHF89s72yr2+lOydzaLGmZuN2UzJf9dGmKs11n1HaN68efGlL30pfud3fifOnz8fd955Z/T29tb7dtBQ8kmuZJOcySe5kk2aoe5GKCLipptuiptuuqlRtUBDySe5kk1yJp/kSjZptEvK5XK5HRdevXp1Oy47LbnXmHt9EbOjxmqzoWY1NsZsqLHabKg59xpzry9idtRYbTbUrMaZy72+8eRed+71RczNGksppdSkWgAAALLU9MdnAwAA5EYjBAAAFE5LG6G9e/fGNddcE93d3bFr165WXnpcd955Z3R0dMS111578diJEyeir68venp6oq+vL06ePNnGCiOOHj0a69evj+XLl0dvb2889NBD2dV5+vTpuOGGG2LlypXR29sb27dvj4iIw4cPx7p166K7uztuv/32OHOm+d+DUS/5nD7ZbA3ZrI98toZ8Tp9stoZs1if3fDY0mw1/iPc4zp07l5YsWZJee+21i89/f/HFF1t1+XF961vfSs8++2zq7e29eOwLX/hCuv/++1NKKd1///3pnnvuaVd5KaWUXn/99fTss8+mlFL68Y9/nHp6etKLL76YVZ3vvPNOevPNN1NKKZ05cybdcMMNaf/+/elTn/pUevTRR1NKKX32s59NX/nKV9pW40Tksz6y2XyyWT/5bD75rI9sNp9s1i/3fDYymy1rhL7//e+nT37ykxfXO3fuTDt37mzV5Sd0+PDhikBeffXV6fXXX08pXQjD1Vdf3a7SatqwYUP6+te/nm2dP/nJT9J1112XDhw4kK644op09uzZlNLYDOREPhtDNhtPNhtHPhtPPhtDNhtPNhsn53zONJst+2jcyMhIXHnllRfXixcvjpGRkVZdflqOHTsWnZ2dERGxaNGiOHbsWJsr+oUjR47Ec889F+vWrcuuzvPnz8eqVauio6Mj+vr6YunSpTF//vyYN+/C11Xl/DuXz5mTzeaQzcaQz+aQz5mTzeaQzcbINZ+NyqaHJUyiVCpFqVRqdxkREfHWW2/Fpk2b4sEHH4zLL7+84mc51HnJJZfEoUOHYnh4OA4ePBgvv/xyW+spghx+7xGyyVg5/N5/Tj6plsPvPUI2GSuH3/vP5ZzPRmWzZY1QV1dXHD169OJ6eHg4urq6WnX5aVm4cGGMjo5GRMTo6Gh0dHS0uaKIs2fPxqZNm+KOO+6IW265JSLyrDMiYv78+bF+/frYv39/nDp1Ks6dOxcRef/O5bN+stlcsjkz8tlc8lk/2Wwu2ZyZ2ZLPmWazZY3Q2rVr45VXXonDhw/HmTNn4rHHHosNGza06vLTsmHDhhgcHIyIiMHBwdi4cWNb60kpxZYtW2LZsmVx9913XzyeU53Hjx+PU6dORUTE22+/Hfv27Ytly5bF+vXr44knnsiixonIZ31ks/lks37y2XzyWR/ZbD7ZrF/u+WxoNps4vzTGU089lXp6etKSJUvSfffd18pLj2vz5s1p0aJFad68eamrqys9/PDD6Y033kif+MQnUnd3d7rxxhvTj370o7bW+J3vfCdFRFqxYkVauXJlWrlyZXrqqaeyqvP5559Pq1atSitWrEi9vb1px44dKaWUXnvttbR27dq0dOnSdOutt6bTp0+3rcbJyOf0yWZryGZ95LM15HP6ZLM1ZLM+ueezkdkspZRSkxo2AACALHlYAgAAUDgaIQAAoHA0QgAAQOFohAAAgMLRCAEAAIWjEQIAAApHIwQAABSORggAACgcjRAAAFA4GiEAAKBwNEIAAEDhaIQAAIDC0QgBAACFoxECAAAKRyMEAAAUjkYIAAAoHI0QAABQOBohAACgcDRCAABA4WiEAACAwtEIAQAAhaMRAgAACkcjBAAAFI5GCAAAKByNEAAAUDgaIQAAoHA0QgAAQOFohAAAgMLRCAEAAIWjEQIAAApHIwQAABSORggAACgcjRAAAFA4GiEAAKBwNEIAAEDhaIQAAIDC0QgBAACFoxECAAAKRyMEAAAUjkYIAAAoHI0QAABQOBohAACgcDRCAABA4WiEAACAwtEIAQAAhaMRAgAACkcjBAAAFI5GCAAAKJx5rbxYqXRpRMxv5SXJ0qlI6aftLqKCbPJzV1xxJt544412l1FBPrnA3km+7J3ka/y9s6WN0IUwbm3tJcnQQLsLqEE2ueCqq55sdwk1yCcR9k5yZu8kX+PvnT4aBwAAFM6kjdDp06fjhhtuiJUrV0Zvb29s3749IiIOHz4c69ati+7u7rj99tvjzJkzTS8WfplskjP5JFeySc7kk1aatBF673vfG88880w8//zzcejQodi7d28cOHAg7r333vj85z8fr776arz//e+PRx55pBX1wkWySc7kk1zJJjmTT1pp0kaoVCrFZZddFhERZ8+ejbNnz0apVIpnnnkmbr311oiI6O/vj927dze3Uqgim+RMPsmVbJIz+aSVpjQjdP78+Vi1alV0dHREX19fLF26NObPnx/z5l141sLixYtjZGSk5msHBgZizZo1sWbNmojI62k3zH6ySc7kk1zJJjmTT1plSo3QJZdcEocOHYrh4eE4ePBgvPzyy1O+wNatW2NoaCiGhoYi4tJ664SaZJOcySe5kk1yJp+0yrSeGjd//vxYv3597N+/P06dOhXnzp2LiIjh4eHo6upqSoEwFbJJzuSTXMkmOZNPmm3SRuj48eNx6tSpiIh4++23Y9++fbFs2bJYv359PPHEExERMTg4GBs3bmxupVBFNsmZfJIr2SRn8kkrTfqFqqOjo9Hf3x/nz5+Pd955J2677ba4+eabY/ny5bF58+b44he/GNddd11s2bKlFfXCRbJJzuSTXMkmOZNPWqmUUkotu1jpg+EbfokYiJReb3cRFWSTn1u9+sl3P1ueD/nkAnsn+bJ3kq/x985pzQgBAADMBRohAACgcDRCAABA4WiEAACAwpn0qXEAAEzHuqr1P7SlCmBi7ggBAACFoxECAAAKRyMEAAAUjhkhAICGMhMEs4E7QgAAQOFohAAAgMLRCAEAAIWjEQIAAApHIwQAABSORggAACgcjRAAAFA4GiEAAKBwNEIAAEDhaIQAAIDC0QgBAACFoxECAAAKRyMEAAAUjkYIAAAoHI0QAABQOBohAACgcOa1uwAAAGAKusuV61fLNU5iqtwRAgAACkcjBAAAFI5GCAAAKBwzQlm6pWr9tbZUAQBARswENZQ7QgAAQOFohAAAgMKZtBE6evRorF+/PpYvXx69vb3x0EMPRUTEiRMnoq+vL3p6eqKvry9OnjzZ9GLhl8kmOZNPciWb5Ew+aaVJG6F58+bFn/3Zn8VLL70UBw4ciC9/+cvx0ksvxa5du+LGG2+MV155JW688cbYtWtXK+otiK9V/aEW2SRn8kmuZLOxFpz7ozF/IhZW/WGq5JNWmrQR6uzsjOuvvz4iIt73vvfFsmXLYmRkJPbs2RP9/f0REdHf3x+7d+9ubqVQRTbJmXySK9kkZ/JJK03rqXFHjhyJ5557LtatWxfHjh2Lzs7OiIhYtGhRHDt2rOZrBgYGYmBg4N3VT2dULIxHNsmZfJIr2SRn8kmzTflhCW+99VZs2rQpHnzwwbj88ssrflYqlaJUKtV83datW2NoaCiGhoYi4tIZFQu1yCY5k09yJZvkTD5phSndETp79mxs2rQp7rjjjrjllgvfcbNw4cIYHR2Nzs7OGB0djY6OjqYW2j7/qmp9tvmXvLVcuX6iXOssoujZJHfySa5ks3FOzPuLxr/pX5cr139YrnHS3CWfTfZkuWK57Pf+Z8X6H0v/o4XFtNekd4RSSrFly5ZYtmxZ3H333RePb9iwIQYHByMiYnBwMDZu3Ni8KqEG2SRn8kmuZJOcySetNOkdoe9973vxV3/1V7FixYpYtWpVRETs3Lkztm3bFrfddls88sgj8aEPfSgef/zxphcLv0w2yZl8kivZJGfySStN2gh99KMfjZRSzZ89/fTTDS8Ipko2yZl8kivZJGfySStN66lxxdSCmaBqZoIAYA75/ar13018esFmgmixm8sVy39sTxVZmPJT4wAAAOYKjRAAAFA4GiEAAKBwzAgBADTVJDNBQFu4IwQAABSORggAACgcjRAAAFA4ZoQm82/KlesD5VpnAdBQV1Wtj0z+ki+WK9f3lWucNA3XVr3+hRm+H7PS/5XeqVjfX/L/IcNc4X/NAABA4WiEAACAwtEIAQAAhaMRAgAACsfDEiZT/XCE365cL973SsV6uPQ3E77d9tgx5tiO2D69mh6uqumuco2TAGaiu2r9aouvf2TSM/4ufadi/UDsq1h/974ZluDhCIW0KH2mYn1/6b9PeP6ZXxv79/p7/mV6f68vOPdHFesT8/5iWq8nZ11V65G2VPHLqv8tWv5s5c9L/22g6hUzrXlh1frYDN+vcdwRAgAACkcjBAAAFI5GCAAAKBwzQtP1jXLFcrg0vZdPex6olrvKk51BES0uV66Hq9fbqs7fVbGsa37tqqprHCnXOmsa/o+q9d9P4TW/WbX+3gxr4IJWzwRNbGt6/5hjv1/6raojfvfM3D9PMhNU7V99rcbBG6d3TTNBc1l7Z4I2pmvGHNtRqvy7fcd/q/x5OrC1Yl36NzP9t2s+M0HV3BECAAAKRyMEAAAUjkYIAAAonALOCLX3Wea/dvqPxxz7l3/91ZbWwBw1XJ7451UzQdXqml8bMxO0oGp9YppvOJWZoGrmQopgoHSy3SVATaUbGzD7S3HdXK5cP1mudVbdfj1+NO3XzHwmaPZwRwgAACgcjRAAAFA4GiEAAKBw5uCM0GQzQO19lrl5ILL1h+Wxx/66xrEJTXcmCKboo+Wxx75b4xi02IfS5jHH/lfpsTZUwqzU4Jmg3Wl/xfoPSh9p6PvPNe4IAQAAhaMRAgAACkcjBAAAFM4cnBFq8QzQx8uV62+Wa52Vtz8uV66/Wq51FrNd9QzQX0+yhpx8tzzmUH+qnAkdLLV3BpS5Kf3Vjop16d9WfsfKVOaBHk/PVqy/HJ+rWH+rdKDO6pj9ypOsp6d6Juja9Htjznmh9NSMrjHG/HLl+lS51llZckcIAAAoHI0QAABQOJM2QnfeeWd0dHTEtddee/HYiRMnoq+vL3p6eqKvry9OnjzZ1CJhPPJJrmSTnMknuZJNWqmUUkoTnfDtb387LrvssvjMZz4TL7zwQkRE3HPPPbFgwYLYtm1b7Nq1K06ePBkPPPDA5BcrfTAitjak8Nmrv8axwZZX0V4DkdLrDXmnRuVTNvm51aufjKGhoRm/j71zmlaVK9eHyrXOmthcmNmckL1zttoelXNGO2L7OGfOXvZO8jX+3jnpHaGPfexjsWDBgopje/bsif7+C/+g7+/vj927dzegSJg++SRXsknO5JNcySatVNdT444dOxadnZ0REbFo0aI4dmz8J/UMDAzEwMDAu6uf1nM5mJap5lM2aTV7Jzmzd5IreyfNMuOHJZRKpSiVSuP+fOvWrTE0NPTu7dJLZ3o5mJaJ8imbtJO9k5zZO8mVvZNGquuO0MKFC2N0dDQ6OztjdHQ0Ojo6Gl3XrPWf0lsV6/9Yuqxi/S/nrhnzml+b9383taaikc/6bE3vH3NsoNTsgdTbqtaPN/l67VWsbFbPQOyoedZFV1WtD1W/29jXj5mzmHMzQa1VrHy21lycCWqlQmXzd8uV673lWmf9ws1VP3+ycv18jb1zZVUe0+9XfVfW3xUnr3XdEdqwYUMMDl4Y8B8cHIyNGzc2tFjwi4UAAAiWSURBVCiYCfkkV7JJzuSTXMkmzTJpI/TpT386PvKRj8QPfvCDWLx4cTzyyCOxbdu22LdvX/T09MQ3vvGN2LZtWytqhTHkk1zJJjmTT3Ilm7TSpB+Ne/TRR2sef/rppxteDEyXfJIr2SRn8kmuZJNWqmtGiPFVzwRVMw9Erpo/D1TL3J4JKrZJZoKq7a56ClTV5+L/7KOfG/uay748vWtARMQflCvXu8u1zmqoTam7Yv3/lV5t+jWZpSabCapWNRNUrXoeqJYizQRVm/FT4wAAAGYbjRAAAFA4GiEAAKBwzAjBrHV51frHjb/Ew+XK9V3lGidBI/x55fJ3K5dvxT2tK4W5rQUzQdXMBEGe3BECAAAKRyMEAAAUjkYIAAAoHDNCMGs1YSao2ldn+gZ/UrX+85pnweT+tN0FUBCL0mcq1v9c+u9tqgRoNneEAACAwtEIAQAAhaMRAgAACseMEDC+ofIM38BMEDC7mAmC4nBHCAAAKByNEAAAUDgaIQAAoHA0QgAAQOF4WAIAwHi+Ua5c/3a5xknAbOSOEAAAUDgaIQAAoHA0QgAAQOGYEQIm8KtV67fbUgVA2/x2ud0VAE3ijhAAAFA4GiEAAKBwNEIAAEDhmBEaozzJGorETBAAMDe5IwQAABSORggAACgcjRAAAFA4ZoTGKLe7AAAAoMncEQIAAApnRo3Q3r1745prronu7u7YtWtXo2qChpBPciWb5Ew+yZVs0mh1N0Lnz5+Pz33uc/H3f//38dJLL8Wjjz4aL730UiNrg7rJJ7mSTXImn+RKNmmGuhuhgwcPRnd3dyxZsiTe8573xObNm2PPnj2NrA3qJp/kSjbJmXy2Sn/VHyYjmzRD3Y3QyMhIXHnllRfXixcvjpGRkYYUBTMln+RKNsmZfJIr2aQZmv7UuIGBgRgYGHh39dNmXw6mTDbJmXySK9kkZ/LJdNR9R6irqyuOHj16cT08PBxdXV1jztu6dWsMDQ3F0NBQRFxa7+VgWqaST9mkHeyd5MzeSa7snTRD3XeE1q5dG6+88kocPnw4urq64rHHHou//du/nfA1V1xxJq666sk4fvx4fOADH6j30i2Re4251xcxfo1Hjpxp+rWnm0/ZbKzZXOORI0eael17Z3vlXl+EvbNZ8qrxyZpH86pxrInqs3fWL/f6ImZ3jRPunWkGnnrqqdTT05OWLFmS7rvvvim/bvXq1TO5bEvkXmPu9aXU/hrryWe7a54KNTZGO2u0d7ZP7vWl1P4a7Z3tk3uN7a5vru6dudeX0tytcUYzQjfddFPcdNNNM3kLaBr5JFeySc7kk1zJJo02oy9UBQAAmI0uKZfL5XZcePXq1e247LTkXmPu9UXMjhqrzYaa1dgYs6HGarOh5txrzL2+iNlRY7XZULMaZy73+saTe9251xcxN2sspZRSk2oBAADIko/GAQAAhdPSRmjv3r1xzTXXRHd3d+zatauVlx7XnXfeGR0dHXHttddePHbixIno6+uLnp6e6Ovri5MnT7axwoijR4/G+vXrY/ny5dHb2xsPPfRQdnWePn06brjhhli5cmX09vbG9u3bIyLi8OHDsW7duuju7o7bb789zpxp/uNf6yWf0yebrSGb9ZHP1pDP6ZPN1pDN+uSez4Zms+HPrhvHuXPn0pIlS9Jrr72Wfvazn6UPf/jD6cUXX2zV5cf1rW99Kz377LOpt7f34rEvfOEL6f77708ppXT//fene+65p13lpZRSev3119Ozzz6bUkrpxz/+cerp6UkvvvhiVnW+88476c0330wppXTmzJl0ww03pP3796dPfepT6dFHH00ppfTZz342feUrX2lbjRORz/rIZvPJZv3ks/nksz6y2XyyWb/c89nIbLasEfr+97+fPvnJT15c79y5M+3cubNVl5/Q4cOHKwJ59dVXp9dffz2ldCEMV199dbtKq2nDhg3p61//erZ1/uQnP0nXXXddOnDgQLriiivS2bNnU0pjM5AT+WwM2Ww82Wwc+Ww8+WwM2Ww82WycnPM502y27KNxIyMjceWVV15cL168OEZGRlp1+Wk5duxYdHZ2RkTEokWL4tixY22u6BeOHDkSzz33XKxbty67Os+fPx+rVq2Kjo6O6Ovri6VLl8b8+fNj3rwLX1eV8+9cPmdONptDNhtDPptDPmdONptDNhsj13w2KpseljCJUqkUpVKp3WVERMRbb70VmzZtigcffDAuv/zyip/lUOcll1wShw4diuHh4Th48GC8/PLLba2nCHL4vUfIJmPl8Hv/OfmkWg6/9wjZZKwcfu8/l3M+G5XNljVCXV1dcfTo0Yvr4eHh6OrqatXlp2XhwoUxOjoaERGjo6PR0dHR5ooizp49G5s2bYo77rgjbrnllojIs86IiPnz58f69etj//79cerUqTh37lxE5P07l8/6yWZzyebMyGdzyWf9ZLO5ZHNmZks+Z5rNljVCa9eujVdeeSUOHz4cZ86cicceeyw2bNjQqstPy4YNG2JwcDAiIgYHB2Pjxo1trSelFFu2bIlly5bF3XffffF4TnUeP348Tp06FRERb7/9duzbty+WLVsW69evjyeeeCKLGicin/WRzeaTzfrJZ/PJZ31ks/lks36557Oh2Wzi/NIYTz31VOrp6UlLlixJ9913XysvPa7NmzenRYsWpXnz5qWurq708MMPpzfeeCN94hOfSN3d3enGG29MP/rRj9pa43e+850UEWnFihVp5cqVaeXKlempp57Kqs7nn38+rVq1Kq1YsSL19vamHTt2pJRSeu2119LatWvT0qVL06233ppOnz7dthonI5/TJ5utIZv1kc/WkM/pk83WkM365J7PRmazlFJKTWrYAAAAsuRhCQAAQOFohAAAgMLRCAEAAIWjEQIAAApHIwQAABSORggAACgcjRAAAFA4GiEAAKBw/jf6pYUgH0kmgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,5, figsize=(15, 6), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = .5, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    axs[i].imshow(images1[i], interpolation='nearest', origin='low', cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 'Background' jet images and their plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Time is  3.978144852910191 seconds for  10000  samples.\n"
     ]
    }
   ],
   "source": [
    "tic = time.perf_counter()\n",
    "isSignal2 = False\n",
    "images2, radii2, z2, theta2, labels2, avgImages2 = makeJetImages(grid, n_data, n_particles, isSignal2)\n",
    "toc = time.perf_counter()\n",
    "print('Processing Time is ',toc-tic, 'seconds for ',n_data, ' samples.' )\n",
    "np.savez('data/jetImages_bkg', jetImages = images2, radiilab = radii2, zlab = z2, thetalab = theta2, labels = labels2, sumedImage = avgImages2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average background image - log scale norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im1 = plt.imshow(avgImages2, interpolation='nearest', origin='low', norm=LogNorm(), cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Individual 'background' jet images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,5, figsize=(15, 6), facecolor='w', edgecolor='k')\n",
    "fig.subplots_adjust(hspace = .5, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    axs[i].imshow(images2[i], interpolation='nearest', origin='low', cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating samples for Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "isSignal3 = True\n",
    "images3, radii3, z3, theta3, labels3, avgImages3 = makeJetImages(grid, n_data, n_particles, isSignal3)\n",
    "np.savez('data/jetImages_signal_test', jetImages = images3, radiilab = radii3, zlab = z3, thetalab = theta3, labels = labels3, sumedImage = avgImages3)\n",
    "\n",
    "isSignal4 = False\n",
    "images4, radii4, z4, theta4, labels4, avgImages4 = makeJetImages(grid, n_data, n_particles, isSignal4)\n",
    "np.savez('data/jetImages_bkg_test', jetImages = images4, radiilab = radii4, zlab = z4, thetalab = theta4, labels = labels4, sumedImage = avgImages4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}