latency-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_CONTAINER = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_csv_data(file_name, data_container=DATA_CONTAINER):\n",
    "    csv_path = os.path.join(data_container, file_name)\n",
    "    return pd.read_csv(csv_path)\n",
    "\n",
    "def max_latency(core1_max, core2_max, core3_max, core4_max):\n",
    "    i1_max = 0\n",
    "    i2_max = 0\n",
    "    i3_max = 0\n",
    "    i4_max = 0\n",
    "    for i in range(len(core1_max)-1,0,-1):\n",
    "        if(core1_max[i] != 0):\n",
    "            i1_max = i\n",
    "            break\n",
    "    for i in range(len(core2_max)-1,0,-1):\n",
    "        if(core2_max[i] != 0):\n",
    "            i2_max = i\n",
    "            break\n",
    "    for i in range(len(core3_max)-1,0,-1):\n",
    "        if(core3_max[i] != 0):\n",
    "            i3_max = i\n",
    "            break\n",
    "    for i in range(len(core4_max)-1,0,-1):\n",
    "        if(core4_max[i] != 0):\n",
    "            i4_max = i\n",
    "            break\n",
    "    max_latency = np.max([i1_max, i2_max, i3_max, i4_max])\n",
    "    return max_latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = \"model_b\"\n",
    "model = \"model_b+\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "latency_rt = load_csv_data(file_name='histogram_xenokernel_' + model + '.csv',data_container='data/4.14.52/')\n",
    "latency_rt = latency_rt.set_index('time')\n",
    "max_latency_rt = max_latency(latency_rt.core1.values,latency_rt.core2.values,latency_rt.core3.values,latency_rt.core4.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "latency_std = load_csv_data(file_name='histogram_stdkernel_' + model + '.csv',data_container='data/latency')\n",
    "latency_std = latency_std.set_index('time')\n",
    "max_latency_std = max_latency(latency_std.core1.values,latency_std.core2.values,latency_std.core3.values,latency_std.core4.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "latency_rt_docker = load_csv_data(file_name='histogram_rtkernel_docker.csv')\n",
    "latency_rt_docker = latency_rt_docker.set_index('time')\n",
    "max_latency_rt_docker = max_latency(latency_rt_docker.core1.values,latency_rt_docker.core2.values,latency_rt_docker.core3.values,latency_rt_docker.core4.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f574234a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnplt = 0\n",
    "mxplt = 400\n",
    "plt.semilogy(latency_std.index[mnplt:mxplt], latency_std.core1[mnplt:mxplt], 'r', label='core1')\n",
    "plt.semilogy(latency_std.index[mnplt:mxplt], latency_std.core2[mnplt:mxplt], 'b', label='core2')\n",
    "plt.semilogy(latency_std.index[mnplt:mxplt], latency_std.core3[mnplt:mxplt], 'g', label='core3')\n",
    "plt.semilogy(latency_std.index[mnplt:mxplt], latency_std.core4[mnplt:mxplt], 'k', label='core4')\n",
    "plt.title('Latency using Standard Raspbian Kernel on ' + model + ' (4.14.27-v7+)')\n",
    "plt.ylabel('Number of latency samples')\n",
    "plt.xlabel('Latency (us), max %i us' % max_latency_std)\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qD3DX+u/ien1fi7Ng3hTn2v0b3XNlJ7u8uKjq34CFuPbvHcAE3DsBVd2D63x1C84FOwnfNVTVP+PeGY96Mq7AWRud5WHcR/1e3IvyK108HD/34Kwb1LEz9qN52MbHnkJFnMvf79Jcg3u3jgQWe8tjVbWxVVl7gai3HvHKqvRZqDOB13Fes9eBD/Bctd7H8zdx74+dEt+1mgg/Bm7wXJj/iXt3/gJ3zbbhFOFZqro9kXPRBo/gPjpe8u6LJkTkOJy3Lu7QjwSZB5SoajyPUAtEtTNemb6LiLwEPKyqfSoyjoh8C9ep5+QOM/chxI0P+4bnfj6oEREFJqlrlzaMLiEirwFXaJyAAkbfQkTewnUCW9FR3oNikLHnbjwK10W8t2UZjnNFvIH7Ev4u0OE4QsMw+jeqGjfKjNH3UNWEPWz9PuKOiPwO5xpa4Llle5t0nOulAngJ58Zrq1ecYRhJxnNlVsb5ddataBgHj7vVMAzDMJJNv7ckDcMwDCMoDoo2yc5SVFSk48aN69K+VVVVZGdnJ1egJNBX5YK+K5vJ1TlMrs5xMMr17rvv7lHVzgSZ6P9oByF5DqYfrtvvvRMnTtSusmTJki7vGyR9VS7VviubydU5TK7OcTDKBbyjfeBd3pO/AeVuVdVnVfXS/Pz8jjMbhmEYA54BpSRFZJ6I3Lt///7eFsUwDMPoBwwoJWmWpGEYhtEZBlTHHRGZB8ybOHFih3kNwxjYNDQ0sHXrVmprW0+mkhj5+fmsWrUqyVJ1n0TkCofDjBo1irS0A+auH3AMKCWpqs8Cz86ePfuS3pbFMIy+zdatW8nNzWXcuHGIdH4yl4qKCnJzczvO2MN0JJeqUlpaytatWxk/fnwPStY3GVDuVsMwjESpra1l8ODBXVKQ/RkRYfDgwV22oA82BpSStI47hmF0hoGmIGMM1OOOx4BSkr3ZcWfxusWs37e+44yGYRhGn2FAKcne5Iw/nMGkuyb1thiGYQxgSktLOeuss8jJyeGKK5I5H/zBy4DquNOrvVtfhuiYaM/XaxjGgKKxsZHU1Piv9nA4zA033MD69etZsaLDqRQNBpgl2Vvu1vLyCCwBftej1RqG0c958MEHmTZtGtOnT+fCCy9k48aNnHLKKUybNo1TTz2VzZs3AzB//nwuu+wy5syZwzXXXENVVRVf//rXOeaYY5g5cyZPP/00ANnZ2Rx33HGEw+HePKx+xYCyJHuLI2Zs720RDMPoDgsWwPLlndolMxKBUKjtDDNmwB13tLl55cqV3Hzzzbz++usUFRWxd+9evva1rzX97rvvPr7zne/w1FNPAW7Iyuuvv04oFOL666/nlFNO4b777qOsrIxjjjmGT33qU30y4HpfZ0BZkr3Flm0b3YJ1GDMMI0FeeuklzjvvPIqKigAYNGgQb7zxBl/60pcAuPDCC1m6dGlT/vPOO4+Qp5RfeOEFbrnlFmbMmMHcuXOpra1tsjqNzjGgLMlea5NM8W5O83AYRv+kHYuvLWp6OJiA30pUVZ544gkmT57cY/UfrAwoS7LXhoDIVvdvStIwjAQ55ZRTePzxxyktLQVg7969HH/88Tz66KMA/OEPf+DEE0+Mu+/pp5/OXXfdFZsikH/+8589I/RByICyJHuLPPmQcjAlaRhGwkydOpXvfe97nHzyyYRCIWbOnMldd93FRRddxK233sqQIUO4//774+67cOFCFixYwLRp04hGo4wfP57nnnsOgCOOOIKKigrq6+t56qmneOGFF5gyZUpPHlq/wpRk0JSXk6Zb3LL2riiGYfQvYp10/Lz00ksH5HvggQdarGdmZnLPPffELXPFihV9MqZsX2VAuVt7hW3bqNcyt9xAk/vDMAzD6Pv0e0tSRE4Evow7limqenwvi9SSbduISL1bboD6SD0ZqRm9K5NhGIaREH3SkhSR+0Rkl4isaJV+hoisEZF1InIdgKq+qqqXAc/RF4frb9tGlAa33AiV9ZW9K49hGIaRMH1SSQIPAGf4E0QkBPwCOBOYAlwgIv7W5i8BD/eUgAmzezdKo1tugKqGqt6VxzAMw0iYPuluVdVXRGRcq+RjgHWquh5ARB4FzgY+FJExwH5VrWirTBG5FLgUoLi4mJKSki7JVllZ2al9x6xeTdSnJF969SXG5YzrUt3JlKsn6auymVydY6DJlZ+fT0VFm6+UDolEIt3aPygSlau2trZPXu+epk8qyTYYCWzxrW8F5njLFwPx+0J7qOq9wL0As2fP1rlz53ZJiJKSEjq1b0lJsyUJTJoyiRMOOaFLdSdVrh6kr8pmcnWOgSbXqlWrutULtKKHgwkkSqJyhcNhZs6c2QMS9W36qru1U6jqjar6ekf5emXS5YYGVJuV5L7KfT1Xt2EYho8XX3yRk046iSOPPJJZs2bFHU5itKQ/WZLbgNG+9VFeWt+mvr6FJbm3fG8vCmMYxsFOe1NlFRUV8cc//pFDDz2UFStWcPrpp7NtW99/jfYm/cmSXAZMEpHxIpIOfBF4ppdl6phWlmRpWVkvCmMYRn8i2VNlzZw5k+HDhwMuok9NTQ11dXW9dnz9gT5pSYrII8BcoEhEtgI3qupvReQKYDEQAu5T1ZWdKVdVnwWenT179iXJlrlN6utRjbiQdLWwt8x6txpGf6MLM2URiWR2Z6aswKfKeuKJJzjqqKPIyLBx2+3RJ5Wkql7QRvoiYFFXy+2VWUAaGkAjkAHUwv791T1Xt2EY/Za2psp68sknATdV1jXXXNOUv/VUWc888wy33XYbQNNUWYcffjjgFPC1117LCy+80JOH1C/pk0oyKHrFkmxoAI06S3I/7C83JWkY/Y0uzJRFRUVNn5wqa9u2bZxzzjk8+OCDTJgwocfk66/0pzbJbtMrvVvr6yEagXQ343KFKUnDMBIgiKmyysrKOO+887jllls44YTkD0U7GBlQSrJX5pNsaIBoFEl3bpCKypqeq9swjH6Lf6qs6dOnc9VVV3HXXXdx//33M23aNB566CHuvPPOuPsuXLiQhoYGpk2bxtSpU1m4cCEAd999N+vXr+f73/8+M2bMYMaMGezatasnD6vfMaDcrb1BY20doKSkpRGhkeoqU5KGYSRGsqfKuuGGG7jyyiv7ZJCDvsqAsiR7w91aU+uCm6empQFQXWPdrQ3DMPoLA0pJ9oa7tTqmJL3BvXW1piQNwzD6CwNKSfYGNfURANLSTUkahmH0NwaUkuwVd2udi7aTke4G7NbV1/dY3YZhGEb3GFBKsjfcrTV1zpJMT08HoMFCQBmGYfQbrHdrQNS/9CLRxoZmd2taBqRAQ70pScMwjP7CgLIke5KpT/0bBa+eRW1MSaZmQKrQaO5WwzB6ibfffpsTTjiBGTNmMH36dP785z/3tkh9HrMkA2LdYPdfXVEJQHpaGFKExoaGXpTKMIyDnfamyjriiCN4+eWXKSwsZMeOHUyfPp158+a1md8YYJZkb3TcWY+bPzI9LYyEhEhjYwd7GIZhOJI9VVZWVlaTQqytrUVEeu3Y+guBfT6IyHnAX1W1QkRuAI4CblbVfwRVZ0f0ZIDzjEaoS4U1gyOwHzIyspCQEI2YJWkY/Y0Ff13A8p2dmysrEok0zcoRjxnDZnDHGW1HTg9qqqxly5bxH//xH2zatImHHnrIrMgOCNKSXOgpyE8AnwJ+C/xfgPX1KSZVuSEfG/KdUgxnZCEpQjRilqRhGB3T1lRZX/rSlwA3VdbSpUub8reeKuuWW25hxowZzJ07t2mqLICjjz6alStXsmzZMn784x9TW1vbw0fWvwjyEyLi/Z8F3KuqfxGRmwOsr08Ri76/Ia0QqCCclUtKSGiMRNrf0TCMPkd7Fl9bVFRU9MmpsmIcfvjh5OTksGLFCmbPnt0TIvZLgrQkt4nIPcD5wCIRyQi4vj5FvefqX5M2AoDMvEGkpKSgkQie/jQMw2iTIKbK2rBhA41ev4hNmzaxevVqxo0bF/CR9G+CtCS/AJwB3KaqZSIyHLg62ZWISArwAyAPeEdVf5fsOrpCrXjfA6EKAMKZzpIkEqGuDsLhXhTOMIw+j3+qrFAoxMyZM7nrrru46KKLuPXWWxkyZAj3339/3H0XLlzIggULmDZtGtFolPHjx/Pcc8+xdOlSfvSjH5GRkUFKSgq//OUvm9y5RnwCU5KqWi0iu4BPAGuBRu+/Q0TkPuAzwC5VPcKXfgZwJxACfqOqtwBnA6OAUmBrUg+iG9TFeo2F3BCQrHA2oVAK1EeoqDAlaRhGxyR7qqwLL7yQz33uczZVVicIzP0pIjcC1wL/5SWlAb9PcPcHcFaov7wQ8AvgTGAKcIGITAEmA6+r6lXAt7oveXJoaFKSVQBkh72u19EoFRW9KJhhGIaRMEG6W88BZgL/AFDV7SKS0OeLqr4iIuNaJR8DrFPV9QAi8ijOitwCxMLYtNkrRkQuBS4FKC4upqSkJNHjaEFlZWVC+9aneA2PKU5J7t65h1BIIBJlyZJlbN5c1aX6uytXb9BXZTO5OsdAkys/P5+KbnzRRiKRbu0fFInKVVtb2yevd08TpJKsV1UVEQUQkeyOduiAkTiFGGMrMAfnfr1LRE4EXmlrZ1W9V0R2APNyc3NnzZ07t0tClJSUkMi+jX/1FkI1AEw9fCpLMjMgUsFhE2Zwwty0LtXfXbl6g74qm8nVOQaaXKtWreqWW7Kne7cmSqJyhcNhZs6c2QMS9W2CVJKPeb1bC0TkEuDrwK+TXYmqVgMXJ7vc7hIJRd2CNywyJzOH9HA6RJSK9bth7ojeE84wDMNIiMDaJFX1NuBPwBO4dsP/VtW7ulHkNmC0b32Ul9YZmXpsqqxIiqckvQA7BTn5pGeFnZL86OPA6zcMwzC6T6DxiFT1ReDFJBW3DJgkIuNxyvGLwJc6U4CIzAPmTZw4MUkitYEq+lgDlAETgVRnSYbzsqFRqdhQGmz9hmEYRlJIuiUpIhUiUh7nVyEi5QmW8QjwBjBZRLaKyMWq2ghcASwGVgGPqerKzsjWU5ZktKEe/gXsAqqBNMhKDxPOzYIIlG8pC7R+wzCM9ti8eTM5OTncdtttvS1KnyfplqSqdrulWlUvaCN9EbCoq+X2lCXZWFfTvPIRkO6UZFaWi+e6b8e+5FS0ahV88AF84QvJKc8wjH5Pe1Nlxbjqqqs488wze0ii/k2gYeJE5CgR+Y6I/IeI9Ho3qZ6yJBvqa6DQW6kA0iE7PUxmposgsG/7Hvbu3cbNr9xMVKNdr+j44+H888HmqDSMg5JkT5UF8NxzzzF+/HimTp3aW4fVrwhyqqz/Bs4DnvSSHhCRx1W114Kc95wlWdvy8yMNcjKyyMhwlmRpQyNX/OqzPNLwD+aMnMNpE07rWkVlntt23bruCWwYRrssWLCA5cuTPFXWjBnccUfPTpWlqtx+++289NJL5mpNkCAtyS8DR6vqjap6I3AscGGA9XVIj1mSddVNQz8ApyTDmWRlZwFQVpBP6Y71ADRGuzF11tix7n9lp5pmDcPoBwQxVdZNN93E5ZdfTk5OTs8fUD8lyN6t24EwEJusLINODtlINj1mSTbUtYz9E0ojNRVy8tyNuS8/i6yK/VAEkWiEa6+9llNOuYiyssM4//xOVDRqFGzaBB9+CCedlNRjMAyjmfYsvrboi1NlvfXWWzz++OPceOONlJWVkZKSQjgc5oorrugxOfsbQVqS+4GVIvKAiNwPrADKROTnIvLzAOttkx5tk/QbiJ6SHDHEBRDYm7OPKC5s3fvL3+enP/0p8+dfxgUXQFVnotVFvfbMDz9MjuCGYfQZgpgq69VXX2XFihVs3LiRBQsWcP3115uC7IAgLck/e78YJQHW1adorK9taUmmZJCaCnMOmQNAaeaHFHvxz1evXA1ATU0BqrBiBcyZk2BFlW6GETZtSo7ghmH0GYKYKsvoPEFOldUn5nXsDerr61pYkpnZYXJyYPIo5/ooD31IxLPh137gZg/bv38oAO+91wklGQtSvLXPzBBmGEYSSfZUWX5uuumm7oo3IAhyqqzPiMg/RWRvZ4MJBCjTPBG5d//+/YHWU1lZiedNBWDEyCxSUqCgoACA+qx/8NoYt23Dys1eLndq3n+/nYKrq+E3v4EXX4xV5P537EDPGYhCAAAgAElEQVQibU6AYhiGYXSRINsk7wC+BgxW1TxVzVXVvADr65CeapOsKPeCCXhzSmaGMwHIy3OHn1LV3MC+vzQ2ZU0Zhx0GixdDm/rukUfgkkvgs59165WVkJcHkQhpe/cm+zAMwzAGPEEqyS3ACo21HA8gyipc75uUdNcdOzfN9XALhULk5OSQWdU8A0h9tev8O23WTn7wAzfk0Rv21ExNDZSXw3o3bITaWqcga2vhsMMAyNi9O8AjMoyByQB8fQED97jjEaSSvAZYJCL/JSJXxX4B1tdnqKx0lmRmmhvycfzI45u2FRQUkNnoWZIK0XoXLWfrx1s45xzIzYWmeU7XrnXWaHY25OfD5s1N5bDadfiJKcmwKUnDSCrhcJjS0tIBpzBUldLSUsLhcG+L0icIsnfrD4FK3FjJ9ADrSZieGidZXlUNQHp6BlWAqDRty8/PR6Pet0kD4I3iqKmoIRSCyZNhzRov85NesKLYQ/ryy82VxIZ9xCzJXbuSfyCGMYAZNWoUW7duZXcXP0Bra2v7pKJJRK5wOMyoUaN6SKK+TZBKcoSqHhFg+Z1GVZ8Fnp09e/YlQdZTWV0HQEaGa4tsbGzu6pqfn0/9Xi9UVZ2XmAG1VbWoKocdJs268KOPWha8ZYuL1/r66y64ObioO/n5ZG7r1TgNhnHQkZaWxvjx47u8f0lJCTNn9nrI6gPoq3L1VYJ0ty4SkX8LsPw+S1W1c7fOmjWXQw89lMsvv7xpW0FBARVl2+CmaJOSTCsMo41KbW0tkyfDli3qggq8+eaBhR93nPuPhaLLzYXJk8nasiXAIzIMwxiYBKkkvwX8VURq+soQkJ6iqsZ1xhk6ZBhr1qzB794dOnQoO3euBxYyaK1zlQ4ZNgyAbdu28etfT4OUMH9Z9B6sWsVSTiCKwNlnuwJycmDYsGYlmZMDkyeTaUrSMAwj6QSmJL0hHymqmtlXhoD0FNW1zkSMDf3w86Mf/chbWoG8eC0Ao0YOAeD7P/w+mzd/ANF6vv3UWfz31LGcyFKe50y45x648Ub41rdg5Mjmnq6ekgzv3t3JmHaGYRhGRwQ9n2ShiBwjIifFfgHUMVdEXhWRX4nI3GSX3xWqa+sByMrKOmDb8OHDOfnkTwJ7KI26b4ZDhw4C4A8P/oFBwwcDULp/Gz84x7VJbrztT1BcDDfd5P6HD28usKDA9fYBi+FqGIaRZIKMuPMN4BVgMfA/3v9NCe57n4jsEpEVrdLPEJE1IrJORK7zkpXmXrR9Ij5bTZ2zJHOyD1SSAEOHFgGluBjwMKvQTYUTjUYZddRIl8lnFG7f28oi9SvJ0aPhxBPRlBSw2IyGYRhJJUhL8krgaGCTqn4SmAmUJbjvA8AZ/gQRCQG/AM4EpgAXiMgU4FVVPRO4FqeMe50ab+xjdhtKcvDgwYjsIRaK7nhNb7oSe9M/hkyalWRqLdu3tyogpiQHD4ZwGIqL2X/kkfDEE0k9DsMwjIFOkENAalW1VkQQkQxVXS0ikzveDVT1FREZ1yr5GGCdqq4HEJFHgbNVNeZj3IebszIuInIpcClAcXExJU0j9jtHZWVlh/vu3edCxH28a2fcvFVVVaju9USGEUuWQb5b3Rr+mFBamJS1h9LA+5Czk5Ur8ygpaQ7qOqKigkOBOhHe8MovOvZYCu65h7cffJDqMWO6dGxBkcg56w1Mrs5hcnUOk+vgIEgluVVECoCngBdFZB/QnTmdRuJC3TWVD8wRkc8DpwMFwN1t7ayq94rIDmBebm7urLlz53ZJiJKSEjraN5TudPXMGdPj5l2+fDkPPfQQsJlQSiajlq8gbRw07AMGQUoWNJS5AAKDx22ntnZcy3L274fbbycjN7cp/fXdu+Geezhmyxb46le7dGxBkcg56w1Mrs5hcnUOk+vgIMjereeoapmq3gQsBH4LfC6Aep5U1W+q6vmqWtJB3h4JcF7f4Dru5OXnxN1eVFTkLW0iPSMX0tPRQi+pAMakT4YGN8PH9BN2tO1uTW8OZFQ/ZAicdBLccAMUFjYPETEMwzC6TJAddyaISMz9KcA4IH4jXWJsA0b71kd5aZ2RqUemyqprcBF28vJy424fPHiwt7SV9PRMGDeOxmOBs2DZt5dx6hknQNQ132YO3U5pKdTV+QooLnb/rb8GH3oIJkyAsjJ45pmkHY9hGMZAJciOO08AERGZCNyLU3APd6O8ZcAkERkvIunAF4E+qQnqG13HncLC+BZrSyUZhjFjGJEJHA1ThkxhxIihoPsIRUI0Zu4AoMVMWGPHwttvw513tix4zBg3jcjhh8PSpck9KMMwjAFIkEoyqqqNwDnAXap6NTC8g30AEJFHgDeAySKyVUQu9sq6AjeUZBXwmKp2yqfYU+7WBi9Wa/6g+LETmt2t1WRkhCEnh5IH4OHci8hKy+IwL2h5XkUeDamlgGuGbMHRR0NGG/2UTjwRXnsNotFuHolhGMbAJsiOOw0icgFu4uV5XlpaIjuq6gVtpC8CFnVVoJ6aBaQh4pRkYWF8JZmT09xWmZmZCZmZTNoLkwa5WAvTpk0DIFwapm68MyHLOxPQb/p0uPde+PjjlmMqDcMwjE4RpCV5EXAc8ENV3SAi44GHAqyvQ3rKkmyMNEIKZIXjf4P4I/FkZIQhFpHfa2ucNGkSGRkZyC6h1hsm0qlm1NFe063FczUMw+gWQfZu/VBVv6Oqj3jrG1T1J0HVlwhBddzZW7OX7y7+Lg0R1xYZiUYgBUIhiZs/M7M5gk56ehiuugoWL4YzXPyE1NRUpkyZQsP2BqqizpLcvbuac889l3Xr1nUskClJwzCMpBBo7Na+RlCW5NUvXM3P3vwZT6xyEW+i0Wi7ZzYUCpGW5oZvFBeHIRSCf/s3kGalOnr0aBorGqlodEpy5cr3eeKJJ3jZP/FyW8QmS93aJ6L0GYZh9FsGlJIMhGiUnS+8DL+F6opqLynS4ZnNynLWZH5+/BnC8/LyiNRGKG9w7tZt25xVWFFR0bFMsXB1ZkkahmF0iyDHSR4ZVNldJRB36+rVvP/sR7AF3l/qQsdFNQIp8V2tMWLtkuFw20qysbqRivpySGlgxw6n8MrLE1CSIs6aNEvSMAyjWwRpSf5SRN4WkW+LSLA9ZRIkEHdrdTVVEddpNxLN9P7bd7dCc7ukv33ST15eHnXVdaCQNaiMjz5ySvLJJxNQkuDaJc2SNAzD6BZBdtw5EfgyLojAuyLysIicFlR9vca+fVRGnKIr2+/+VaNJsSQjjRFohOyifZSWOoX33nuVick1ZAjs2ZNYXsMwDCMugbZJqupa4AbcNFYnAz8XkdVeUPIeJxB3a2kpkYgLRp6T7SxK1ybZfSUJQB1kDd5LVVXMKqw4MJZrPAYNgn37EshoGIZhtEWQbZLTROR2XHScU4B5qnq4t3x7UPW2RyDu1tJSop6SrPfmkYxGtUMlme4FJ09ESaYP2klDQ7OS3L07AbkKC10sO9UEMhuGYRjxCNKSvAv4BzBdVS9X1X8AqOp2nHV5cFBaCo0RAOq8KOSqkRbDOeKRkuJOfUdKMhwJUzXsOWCnt6WCskSmrh40CCIRqEzQPWsYhmEcQJBh6c4CalQ1AiAiKUBYVatVtVcj7ySTyO69ThkBtfVuiiyNRjtUkuJt70hJHjf0OJbueQKIWYQViXlRC725t/buhdz4s5EYhmEY7ROkJfk3wN91M8tLO6j4aFMqNLpA4nWekoyqIh24W2OWZHu9WwFmFM6gocozHVPTSdiSjClJa5c0DMPoMkEqybCqNvn6vOXuzCfZbYLouPPupqImS7Leb0l2oCQTtSSHhIZATNxh9XTK3QqmJA3DMLpBkEqySkSOiq2IyCygJsD6OiSIjju3b34NYh13GmJtklGQ9k9tom2SOZoDsRlAhkCX3K2GYRhGlwiyTXIB8LiIbAcEGAacH2B9vcKysuealhtilmQC7tZELcmqyipnSYaBXIBK9u1T3CltB7MkDcMwuk1gSlJVl4nIYcBkL2mNqjYEVV9foL62Foh13GnfkowpydTU+JcgHA6TmppKeXk5w2oPY2fuakgHiLJnTzWQ3b4wZkkahmF0m6ADnB8NTAOOAi4Qka8GUYmIZIvIOyLymSDKT5SGOk9JdsKS1DbGMYoIeXl5lJeXM4RRsH8qZLhte/YkEJouOxvS0sySNAzD6AZBBhN4CLgN+AROWR4NzE5w3/tEZJeIrGiVfoaIrBGRdSJynW/TtcBjSRK9c+Q1LzZ44yRJwJKMtUm2pSSBJiXZ2FgJ0UGeJQn79iUY5Hz4cAtybhiG0Q2CbJOcDUzR9rRA2zwA3A08GEsQkRDwC+A0YCuwTESeAUYCH+Ja7XqeNJxDeQ00NjS3SaYk6G5NREnW1VVAZEKTJZmQkgSYOBHWrk0sr2EYhnEAQSrJFbjOOjs6u6OqviIi41olHwOsU9X1ACLyKHA2kINroJsC1IjIIlWNti5TRC4FLgUoLi6mpKSks2IBUFlZeeC+IfdXVeVtiyqItFvHGWecwZIlS4hEIm3mU1U2b97M/v17QWdBWgquTbL0gH3iyXVodjZD3nmH17p4rMki7jnrA5hcncPk6hwm10GCqgbyA5YA+4DFwDOxXyf2Hwes8K2fC/zGt34hcLdvfT7wmUTKnjVrlnaVJUuWtFhnMMpUFNCxc2arqqrkpmn6hKFdriPGpz/9aZ01a5YWFAxSuFxD8wsU0OzsZzuUS1VVb7tNFVRLS7stS3eIK1sfwOTqHCZX5zgY5QLe0YB0Rl/9BWlJ3hRg2Qegqg90lEdE5gHzJk6cmNzKBUiBSKPXeTeqSEr3m3vz8vJYu3YtVVUVQC7pqQXUUEZ1dQWqHUa+g0mT3P/atTBnTrflMQzDGGgEOZ/ky8BGIM1bXoYLeN5VtuHmpowxykvrG4QgEmkEvN6tHbRJJkJeXh579uyhoaEBkRwy0gq98ivwRpu0zyGHuP8NG7oti2EYxkAkyN6tlwB/Au7xkkYCT3WjyGXAJBEZLyLpwBdxLtyE0SCmyoqR0qwknSUZ6naReXl57POGcITDuWRlFHlbKhKb3GPECPe/c2f7+QzDMIy4BDlO8nLgBLygauomYB6ayI4i8gjwBjBZRLaKyMWq2ghcgWvjXAU8pqorOyNQIJMuxwi1UpJJsiRjnHdeLlMnx5RkJRWJdHAtLHRjJU1JGoZhdIkg2yTrVLU+NtRBRFJpnu+pXVT1gjbSFwGLkiZhMkmBSNQFOicKKZIcSzLGZz6Tw6s5RbyYBjRUJKYkRWDYMFOShmEYXSRIS/JlEbkeyBSR04DHgWcDrK9DAnG3qncKUyAaiYDrT5q0jjsxcnNzGZQZCyhQnvhcyqYkDcMwukyQSvI6YDfwAfBNYJGqfi/A+jok6e5WVYiGyK4bBBIiGo1AY6OzJJPUJhkjNzeXwnChCygQKk3MkgRTkoZhGN0gSCX5H6r6a1U9T1XPVdVfi8iVAdbXIUm3JKMuZkFIU0FSiEYjROvqQJPvbs3JyWFw1mBnSabsNSVpGIbRAwSpJL8WJ21+gPX1ONoYaV5JETQapdEbm5EMS7KgoKBpOTc3l+LsYmdJppR1zt26ezds2ULiOxmGYRgQgJIUkQtE5FlgvIg84/stAXp13qZku1s14ixJQZAUIRqN0FBd7dKSoCRnzJjRtJybm0txTrGzJGV/4pbk5MnO4h0zBq67ruP8hmEYRhNB9G59HRevtQj4X196BfB+APUljKo+Czw7e/bsS5JRXqTesyQFkBRnSXozgYRSun9q09LS+Oijj3jqqacoKioiWhV1lqQmOAQE4ItfdDOBXHcdvPJKt2UyDMMYSCRdSarqJmATcFyyy+5rNClJQDx3a0NNDQApSVCSAIcccghXXXUVAEVZRZAJRDqhJEMhuPZa2LwZfv97EotnZxiGYUCwEXeOFZFlIlIpIvUiEhGR8qDqS1CmpLpbow1OSTpDMqVFm2QoCe7W1oRSQmQXZkNjDfv31zel10fqWbRjUfM4zXgcfjiUl8OOTk/KYhiGMWAJsuPO3cAFwFqc/fMN3HyQvUaye7dGGrwZucTND6kapaFJSQYTp6GgyHXm2b17V1Pak6ue5NZ/3crijxa3vePhh7v/VasCkcswDONgJEgliaquA0KqGlHV+4Ezgqyvp/G7W0kJoVFtdreGglGSRcUuNN3HHzdbhO9sfweAZduWtb1jbOYTC3ZuGIaRMEEqyWovEPlyEfmpiPy/gOvrcZrcrQIpXsedulrXuzUUSgukzuHDhgOwfd/KJvfquzveBWDZ9naU5KBB7r+sLBC5DMMwDkaCVFoXAiFcUPIq3DRX/x5gfT1Ok7sV1yaJKrU1npIMoE0S4IhDjgBg/SEX8eOlP2Z31W7+scPNQPbm1jcpq42vBDU7h/vkYvbtdL1vqauDBx90HXkMwzCMuAQ5n+QmVa1R1XJV/R9Vvcpzv/Yaye64E1OSgrMkiWrgluTJU052C5WwcMlCht42lPK6cr485suU15Xzzee+GXe/5e8JF+tv+MwfvNjxV18NX/sa/P3vpigNwzDaIOkNZyLyAe3M9qGq05JdZ6Ike5xkzN2KxHq3KnV1rk0yNSUYJXnsuGNdNyjfEJCbP3kzJ0RPIFQY4sWPXoy73/Ll7v/1nYewezcMiY2ZPO00mD69OYNhGIbRRBCW5GeAee38Dhr87taUlJCzJOtc79bUtGCUZFFWEQzDzahZCxfNuIjvneTixk8vns6Oyh3srtp9wH7//Gfz8vLlwPr1zQnvvReIrIZhGP2dpCtJz83a5i/Z9fUm/iEgMSVZXR2suxXgmvlPu1belTBlyJSm9GnFzkj/YNcHB+yzfDlMzNwKwNlnK/dVnBuYfIZhGAcLB1Vv056mRZukpyRrq50lmZaaHli9nzrOG0lT6VmWHjEluXznga7TdevgpCGrEaLU1AgXc19g8hmGYRws9HslKSKHi8ivRORPIvKtnqy7qU0SIZSaBo1KdWUVAKlpGYHVO2JEOpBFYcMIzp58dlP60OyhTBo0iefXPX/APpWVkJsdRfv/JTcMw+gxgpgF5O/e/0+6UcZ9IrJLRFa0Sj9DRNaIyDoRuQ5AVVep6mXAF4ATuiN7Z4k0ev2TBNLSMyACpdvdIP+MzOzA6i0qAhjCpPpTKcwsbLHt3CnnsmTDEvZU72lKU4XqasjOEa5Iu8fJR23bvasMwzAMIBhLcriIHA98VkRmishR/l+CZTxAq+g8IhLChbU7E5gCXCAiU7xtnwX+AixK1kEkgt/dmpc2FIAVOz8CIDMrN7B6Bw8GGMy+faUHbPvcYZ8johGWbFjSlFZfD5EIZOemcFfDZdxx3mvUEWY3Q5p3tGEghmEYBxBE7LT/BhYCo4CftdqmwCkdFaCqr4jIuFbJxwDrVHU9gIg8CpwNfKiqzwDPiMhfgIfjlSkilwKXAhQXF1NSUpLg4bSksrKyad+P398JQGOkkSG1k/gXf+e1fa7XaGVNfZfrSIRQaBB79uxoqiMmV320nhRSePbtZxmyewildaWk1RUDn6C6fh8AOdteA05gM2MYiusJ+8qLLxJND6Yd1X/O+hImV+cwuTqHyXVwEMRUWX8C/iQiC1X1B0kseiSwxbe+FZgjInOBz+NmWmzTklTVe0VkBzAvNzd31ty5c7skRElJCbF9l290HWRSU1M5dPSRvPYebG1wbZKjxx1CV+tIhKyse2ho2NhUh1+uyasmUxYu4+nap7njrTu4e+4jAIybOByWwqwUdxo3M4bZuJB2J82aFTNRk45ftr6EydU5TK7OYXIdHAQThRtQ1R94btCTvKQSVX0ugHpKgJJkl5sIsTZJESgc6toG66oiEAq2TRIgO3swe/Yc6G4FmD5sOo+ueLRp/dcf3Al8kew8510f+/HbAGxgfPNOlZWBKUnDMIz+SpDzSf4YuBL40PtdKSI/6kaR23DxX2OM8tISJtlTZUUbm8dJDhrmte9VAOmQGuAQEIC8vEE0NpYRiRw4h+Rxo9x810cNP4qbP3kz75W+CTk7yM518WQLN79HUVoZa5jcvFNlZaDyGoZh9EcCsySBs4AZqhoFEJHfAf8Eru9iecuASSIyHqccvwh8qTMFiMg8YN7E2LRR3cQfcadopE9J5kBagMEEAAoLBwPKvn37KCoqarHt8qMv5/QJpzOuYBwlG0tc4uC1ZOd51m1dHYcV7WD1nsOadzIlaRiGcQBBD5or8C0nbL6JyCPAG8BkEdkqIheraiNuRpHFuKBsj6nqys4Ik/RJl2PuVoQhw7xDbQQyIC2g+SRjDB3qFOOuXQeGoAulhJhcNJmM1AwmDvI+CAo/Iiu/WXEfNmgXqwuOheOc1UlVVaDyGoZh9EeCfJP/GPiniCzBjZI4CbgukR1V9YI20hfRjWEeybYk/e7W4uK85g0ZkBoKZqqsGIce6qbMevHFV5k8+dA2843JH0MKIaKD1pGdP6cpfXLRXnb/K53Sm/+PwafOMEvSMAwjDkFOlfUIcCzwJPAEcJyq/jGo+hKUKRBLEmDoUN+4yAxIDdiSPOKII4EcFiz4JjNmzGDv3r1x86WF0hicOhYGfUR2QbMlefgwNxzkwx1eMAJTkoZhGAcQqLtVVXeo6jPeb2eQdSVCUPNJIkJhYSoQduvpMDw8vs39ksHo0SnAGABWrFjBokVtG9iDZQIMWkf2oOZQeTPGOiW5fL1nAZuSNAzDOIABFcgzuDZJyM0F8BTOljMoDo9ua7ekMG4cwMPMn/9rhg4dys6dbX+DDNUjYegKUn2HPaI4wpAhsPxfWS7BlKRhGMYBDCglmWxLMhppbpPMyADwXK7lUwi4SZLRo0FkOmPHfoOxY8fy8ccft5l3WOPxkFrHmsi/mtIkO4uZM+GfKzwXrHXcMQzDOIBAlKSIhERkdRBld4ekW5INLeOdhiQWQCAvcCWZng4jR8LGjTBmzBh27drVZt6i6uMBWLJzaXNiVhZHHgkfrhI0I2yWpGEYRhwCUZKqGgHWiMiYIMrvK/gj7gAMylxILp8G5gWuJMG5XP1KUtsIUi5VwwntmsnCV/+Hvx3iJWZlMXo01NVBadZoU5KGYRhxCNLdWgisFJG/i8gzsV+A9fU40aZYAk5LDs09mSx+CxzVo0py7Nix1NbWttnDtaoKil94EYBXxnqJWVmMHOkWt4UnOCX51a/C1VcHLrdhGEZ/IchxCgsDLLtLJD3ijm8+SYDBufWs/Nhpnp5Sko88AiNHOoN9w4YNDI4Tf7WyEvLTBhMuPIR/DXazlJCVRSxI0La0cUz/8x+gosIl3Hpr8MIbhmH0A4IcJ/kysBFI85aXAf8Iqr4EZQqmd6unJMcMrWvalhrsMEnAKclIBAYPdoEF3nvvvbj59u+H/HyYPHgya2I61GdJbg+NblaQSfqAMAzDOBgIMsD5JcCfgHu8pJHAU0HV1xtEIy0tybEjG5q29ZQlCaA6gezsbN599924+WJK8tDBh/KvwW5ST7KyGD7cbd/GyObMjY1BimwYhtGvCLJN8nLgBKAcQFXXAkMDrK/H8cduBRg7urnjzKhRwdcfU5KbN6cwadIk3nnnnbj5/EqyOh225wJZWaSlwdChsC06rDlzdXXgchuGYfQXglSSdapaH1sRkVQ8I6a3SHrEnVZtkmPHN5/OmAILEjdW0nXemTx5Mu+99x579uw5IF95OeTlwfgCFwVoUwGQ7YarjBwJ2xp83y6mJA3DMJoIUkm+LCLXA5kichrwOPBsgPV1SNLnk4y01PljJjTHRo21UwaJf6zkmWeeSX19PXffffcB+WKW5Kg8Z95uyQOyXKSdkSNhW51vqq3qamhjKIlhGMZAI0gleR2wG/gA+CZu9o4bAqyvx4nNdxxTiGMnu9itl4Qf6jEZ/MNAPvWpT/H444+32N7Q4PRefj6Mzneh8rbk01JJVhc27xCNQn09hmEYRrC9W6PA74AfAP8D/E7bGu3eT2meBcRpycwhOexgGL+c9dsekyGmJAFOPPFEVq1aRXl5edP22GJ+PuRn5JNTB1vzcGYoTknuqcmhjvTmQs3lahiGAQTbu/Us4CPg58DdwDoROTOo+nqDaLRlmyS5uQx7/gFSn/pTj8kwbhxs3QqRiDBnzhxUlZdeeom33noLcK5WcEpSRBhd7rlbPfO3aRgII5raKU1JGoZhOIIczfe/wCdVdR2AiEwA/gI8n+yKRORzwFm4aTh+q6ovJLuOeERioyX87Y9nnNETVTcRGyu5e3cGp58+DYBzzjkHgF27drF/v4sYEGuGHb3fc7d6NEXdYSTj8+tdeB5TkoZhGECwbZIVMQXpsR6oSHRnEblPRHaJyIpW6WeIyBoRWSci1wGo6lOqeglwGXB+MoRPhKY2SXqgl04bxHrR7twZprCwkOLi4qZty5b9o8ndmufN4jXq1M+xZWxBU54RI9z/GxzXnMmUpGEYBhCAkhSRz4vI54F3RGSRiMwXka/herYu60RRDwAtzDIRCQG/AM4EpgAXiMgUX5YbvO09QkxJ9qKObFKSO3a4TkNjx45t2nbOOe/yjBctt8mSHDedjxv3Ux9xnXNiluQ13Mo/Uo9xK6YkDcMwgGDcrfN8yx8DJ3vLu4HMRAtR1VdEZFyr5GOAdaq6HkBEHgXOFpFVwC3A86oaN/SdiFwKXApQXFxMSUlJoqK0oLKysmnf0j0uoHhdXV2Xy+sujY1CYeFxPPbYCE499WWyvF6rAPX17/Czn7nl1avfoqKihqodVSjKEy88wfBMF3LnwiPhoQ/m8mrjVI4Clr/+OmV1dXFq6xr+c9aXMLk6h8nVOUyugwRV7bM/YBywwrd+LvAb3/qFuE5B3wHeBX4FXNZOefOAeydOnKhdZcmSJU3LP/nkIqUgTUcfP6bL5SWDxx9XBdU//1n16quvVlzQBs3Onqpu0KNqebnLu3jdYuUm9JWNrzTtX19Vr3ElBz8AABg2SURBVKGUiH5v/laX+Zlnkiqf/5z1JUyuzmFydY6DUS7gHe0DuqEnf4F13BGR8cB/eIquqR5V/Wyy61LVn+N60XaU71ng2dmzZ1+SjHqb3K29zNlnQ1ZWI88/n8qYMc29crKySrn5ZjjtNMjNdWmj87yxkuVbmvKlZaUxZix8tCvHJcyfD//6F8SZUcQwDGMgEWTHnadws4DchevpGvt1h23AaN/6KC8tIZIdli7aR5RkWhrMmrWP55+HzMxmj3ZZWSlXXqlMndqctynqzv4tLcqYMAE+2ubaNdm7F+JE7jEMwxhoBKkka1X156q6RFVfjv26WeYyYJKIjBeRdOCLQK9N5Nzcu7X3OfzwcrZsgfPPv4Rx475JUdH1NDQ0UFlZ2SJfbkYu+Rn5LSxJcEpy/ZbmsHpNPV0NwzAGMEEqyTtF5EYROU5Ejor9Et1ZRB4B3gAmi8hWEblYVRuBK4DFwCrgMVVdmWiZmuz5JPtA79YYxcWuo01ZWS6Fhb9i+HA3L+RPfvITampqWuSdXDSZN7e+CUBj1A32HD0aSvemNEfeaWjAMAxjoBNkMIEjcR1rTgGiXpp66x2iqhe0kb4IFwe204jIPGDexCRNLByNdpynpxg6tBaAzZth0yY45pjBfPAB/PCHP6S2tpbbbrutKe+Xj/wyV/71SkLfDzGhcAJrrlhDYaHT9PvJZyi7ncvVMAxjgBOkJXkecIiqnqyqn/R+CSnIoAjMkuwDxCzJDz90+m3s2OZON+vWrWuR9yvTvsKwnGFENcravWspqy2jwIsvUFZ4iFswJWkYhhGoklwBFHSYqwdJ+nyS0T7gZ/UYNKiOUAiWLnXrkyY1T3/19NNPc8899zTnzRzE9qu288QXngBgQ9mGpmADZX99E6ZMMSVpGIZBsEqyAFgtIotF5JnYL8D6OiTZlmRU+46SDIVg1Ch4/XW3fuihLYdvXHbZZezbt69pXUSaJmHesG9DsyVZBgwaZErSMAyDYNskbwyw7D5BRIP8xug8Y8bAq6+65UMPLTxg+5NPPsnFF1/ctD6+0FOSZRs4zFOS+/cDhYWucdMwDGOAE+R8ki/H+wVVXyIk3d3ahyxJcEoyxsiRIQoKCrjssst4+umnGT16NIsXL26RvyBcQEG4wCxJwzCMNggy4k4FrjcrQDqQBlSpaq8NwEt2xJ1oH7QkAXJy3M/vXr3vvvv44IMPDthnfMH4lm2SpiQNwzCaCNKSzFXVPE8pZgL/DvwyqPp6g75mSY72YhENH37gtiOPPJK1a9dSW1vbIn1cwTg2lG0gO9u1azYpyaoqSGKQc8P4/+3de3hV1ZnH8e8vGEK5SIRggqIlFIrijSJqrZ0+DE5HxCJ1SqdaH7XWStvROmpbgdrp2Hbq1KmdjlaqdSy1WkRaq4z3SxW0tRdArgG8IFIFAoiIJNIAhnf+WOskJ+GckJPmXJLzfp5nPzl77X32frMIebPWXmct57qinDSF4ty484AzcnG/dDq/u7UwW5JVVfsfO+6442hsbGTNmjUtyqvLq1m/Yz1glJfHZ5LlyQ8onXOueGXtt3xiXcm4TZH0faDhgG/Mou48uhWak2SqluTxxx8PwJIlLVcSqz6kmob3Gtjy7hbKy2HtWmjsG+snsWKzc84VqWw2hSYlbWcAdcDkLN6v6LWVJEeOHMmQIUN4+OGHW5Qnfwzk9dfhiSfg8rkfDQc9STrnilzWBu6Y2cXZurZLrX9/uOIKmDJl/2OSmDx5MrNmzWLXrl1NizMnfwzkhBNOZfFiuO3xamZwBEd6knTOFblOT5KSvtXGYTOz73b2PV2zm25Kf+z0009n5syZrFq1ipNOOgkIA3cgtCQfeQQWL4azzoKFnOxJ0jlX9LLR3fpuig3gEmBaFu7Xbp09cKerGTFiBACvvvpqU1nv0t5U9qnktR2vceihMHp0KN9GhXe3OueKXqcnSTP7YWIDbid8/ONi4F5gWGffL8PYOnXgTlczbFio/tYTnic+BgIwMM5m50nSOeey9ExS0gDgauB84BfAGDN7u+13uWzr3bs3hx12WIuWJITnkgs3LuTGP9xI475GDj74GrbtrAgfAbE4H4QKaySvc87lQqe3JCX9AFhEGM16nJld5wmycAwfPny/lmR1eTXr3l7H15/6OtOfnk5FBWzTofCNb0BlJZSUQF1dniJ2zrn8ycYzya8ChwHfBDZJ2hm3Okmd3n8naZikn0m6r7Ov3R0NHz6cV155pUVZ4mMgCQMrjG3Eftc33wxfN23KRXjOOVdQsvFMssTM3pc8LV3c+rV33lZJsyRtlVTTqnyCpJckrZU0Pd5vnZldkvpKrrVjjz2WLVu2sHXr1qayYw49psU5fQdv4iX7IJtI+sCltySdc0WosOZVa3YnMCG5QFIPYCZwJjAKOE/SqNyH1rUlZt5ZsWJFU9mpQ05l+ZeWc9tZtwHQs3Id66nmcDbBNdeEkzxJOueKUEEmSTN7Dmi9DMXJwNrYctxDGC3rM/hkKFWSlMTxlcczvno8AHUHvdZ0rHHSJ8MLH+nqnCtC2Vx0ubMdDryRtL8BOEXSQOB7wIckzTCz/0z1ZklTgakAlZWVLFiwoENB1NfXN713166wSkZDQ0OHr9dZkuM6kIEDBzJv3jzGjBnTonzvvr0IUdvQvKTWg4s2cw6wZuFCtnTwozOZxJZLHldmPK7MeFzdhJkV5AYMBWqS9qcAdyTtXwDckuE1JwG3Dx8+3Dpq/vz5Ta+/MPJZo7zUjjztyA5fr7Mkx3Ug06ZNM8CWLFmy37FDvn+Iff6+y23qp7YZmD1x73YzMJs5Myex5ZLHlRmPKzPdMS5gsRVAfsjlVpDdrWlsBI5I2h8Sy1yGronPGZ988sn9jvXv1Z89JTu47sdhdOsfVvQNB7y71TlXhLpSklwEjJBULakncC7wYCYXsCKfcSdhwIAB9OvXj9ra2v2Olfcq552Gd6iqgrIy+Pb1pTxd8nEfuOOcK0oFmSQlzQH+CIyUtEHSJWb2HnA58ASwBviVma3K8LpFPXdrsqqqKjZv3rxfef+y/uxo2IEE99wTyl7udZy3JJ1zRakgB+6Y2Xlpyh8FHs1xON3S4MGD07YkE/O4TpoUyraVHgZ1K/c71znnuruCbElmi3e3NquqqkqZJPv3Ci1JgNLSsEblmz2qvCXpnCtKRZUkvbu12eDBg1N2t5aXhWeSCRUVsK1kkCdJ51xRKqok6S3JZlVVVdTV1fHuu++2KO/fqz87d+9kn+0DYpK0Ch+445wrSkWVJF2zwYPDvKybNm3i2muvZeXK8MyxvFc5hvHFh77IPtvHoEGw7rBV3LZvITz2WD5Dds65nCuqJOndrc1GjBgBwNy5c7n++uuZOnUqEEa3Atyx9A5qttZQUQGvfupCvvwJ4K678hWuc87lRVElSe9ubXbiiSdSVlbGDTfcAIQltCC0JBOef/15KiqS3lRfn8sQnXMu74oqSbpmZWVljB07lvqY+EaOHAlAj5IeTec8/0b6JPnssz6WxznX/RVVkvTu1pYmTpzY9LqkJPwoHF1xNAB9SvuwfMtyRiUvRhYH76xfD+PGwWWX5ShQ55zLk6JKkt7d2tKMGTPYsGFDi7KRFSNp/FYjnznmM+xo2NE0oQDA9ndCK3PZsrD/1lu5itQ55/KjqJKka0kSgwYN2q+8RCUtJhVIWLMjjIhdvTrsH3NM1kN0zrm88iTpUirvVU79nnre2/deU9nq+iPD15gkq6ryEZlzzuWOJ0mXUuKjIMmz76xq+ADfmf04szf9G/TZyuy3z2Hn7p1s3w6f/Sxs356vaJ1zLjsKcoLzbJE0CZiU+LiDSy/xUZDN9c1T123nEB584W447T7osZelpfO4ddGtbH9oGnPmwAknwLRp+YrYOec6X1G1JH3gTvv17xXqaMPOlgN7djTWwkF7oLEUgNr6WhLzpB96aE5DdM65rCuqlqRrv0RLsnWS3FWypcV+bX0tb8fGZmVlTkJzzrmc8STpUko8k2ydJPeUxYzYM0yMXltXy9uxJVlWlrPwnHMuJzxJupRStSR39SjB3hdH5/QNmXFz/Wa2778spXPOdQtdPklK6gP8BNgDLDCz2XkOqVtoeiZZ15wkN/QtbT6hb2hR1tbXUu+TCjjnuqmCHLgjaZakrZJqWpVPkPSSpLWSpsfifwLuM7NLgbNzHmw3dXDZwQBs3LmxqWxTXzWf0C80H+v3+KTnzrnuqyCTJHAnMCG5QFIPYCZwJjAKOE/SKGAI8EY8rTGHMXZrB5UcRN+efVn95uqmsjcuuLT5hL5Jfax9tsClJ7OtdiU7dsApp8BVV8GVV8Kyzcv4ytKv8Ne9f81h9M451zkKsrvVzJ6TNLRV8cnAWjNbByDpXmAysIGQKJfRRtKXNBWYClBZWcmCBQs6FFt9fX3Te3ft2g1AQ0NDh6/XWZLjysTevXsBWLdu3X7vP3/I+by480We3fYsNB4EvXZCw8G8v34vfzkkKemNuQMOX8Sc+VewtOZWFi48ioULw6EFQz9Hzc4afvrITxldPrqD3112dLTOss3jyozHlZlCjatQFWSSTONwmluMEJLjKcDNwC2SzgIeSvdmM7tdUi0wqV+/fieOGzeuQ0EsWLCAxHtn934O6qBXr1509HqdJTmuTOzZsweAYcOG7ff+cYT9Cx+4kLuXzg2FT95I6SlXprxWnz69Oeqoo1qUlZeXwzswevRoxg3NPL5s6midZZvHlRmPKzOFGleh6kpJMiUzexe4ON9xOOec634K9ZlkKhuBI5L2h8SydvMZd5xzzmWiKyXJRcAISdWSegLnAg9mcgFfdNk551wmCjJJSpoD/BEYKWmDpEvM7D3gcuAJYA3wKzNblcl1vSXpnHMuEwX5TNLMzktT/ijwaEev66uAOOecy0RBtiSzxVuSzjnnMlFUSdKfSTrnnMtEUSVJb0mmZ2YHOiMncTjnXCHRgX85dj+S3gT+0sG3VwDbOjGczlKocUHhxuZxZcbjykx3jOv9ZjaoM4MpdEWZJP8Wkhab2dh8x9FaocYFhRubx5UZjyszHlf3UFTdrc4551wmPEk655xzaXiSzNzt+Q4gjUKNCwo3No8rMx5XZjyubsCfSTrnnHNpeEvSOeecS8OTpHPOOZeGJ8kMSJog6SVJayVNz3Ms6yWtlLRM0uJYNkDSU5JeiV8PyUEcsyRtlVSTVJYyDgU3x/pbIWlMjuO6TtLGWGfLJE1MOjYjxvWSpDOyGNcRkuZLWi1plaR/jeV5rbM24iqEOuslaaGk5TG2b8fyakl/jjHMjasDIaks7q+Nx4fmOK47Jb2WVGejY3nOfv7j/XpIWirp4bif1/rqsszMt3ZsQA/gVWAY0BNYDozKYzzrgYpWZf8FTI+vpwM35CCOjwFjgJoDxQFMBB4DBHwY+HOO47oO+FqKc0fFf88yoDr+O/fIUlyDgTHxdT/g5Xj/vNZZG3EVQp0J6BtflwJ/jnXxK+DcWH4b8OX4+l+A2+Lrc4G5OY7rTmBKivNz9vMf73c1cA/wcNzPa3111c1bku13MrDWzNaZ2R7gXmBynmNqbTLwi/j6F8Ans31DM3sO2N7OOCYDd1nwJ6Bc0uAcxpXOZOBeM9ttZq8Bawn/3tmIq9bMlsTXdYRl3w4nz3XWRlzp5LLOzMzq425p3AwYD9wXy1vXWaIu7wNOl6QcxpVOzn7+JQ0BzgLuiPsiz/XVVXmSbL/DgTeS9jfQ9i+RbDPgSUkvSJoayyrNrDa+3gxU5ie0tHEUQh1eHru6ZiV1R+clrtit9SFCC6Rg6qxVXFAAdRa7DpcBW4GnCC3XHRbWmW19/6bY4vF3gIG5iMvMEnX2vVhnP5JU1jquFDF3tv8BrgH2xf2BFEB9dUWeJLuuj5rZGOBM4DJJH0s+aKHvJO+f7ymUOKJbgQ8Ao4Fa4If5CkRSX+A3wJVmtjP5WD7rLEVcBVFnZtZoZqOBIYQW61H5iKO11nFJOhaYQYjvJGAAMC2XMUn6BLDVzF7I5X27K0+S7bcROCJpf0gsywsz2xi/bgUeIPzi2JLovolft+YpvHRx5LUOzWxL/KW2D/hfmrsHcxqXpFJCIpptZvfH4rzXWaq4CqXOEsxsBzAfOJXQXZlYOD75/k2xxeP9gbdyFNeE2HVtZrYb+Dm5r7PTgLMlrSc8FhoP3EQB1VdX4kmy/RYBI+IIsZ6EB9wP5iMQSX0k9Uu8Bv4RqInxXBRPuwj4v3zE10YcDwIXxlF+HwbeSepizLpWz3/OIdRZIq5z4yi/amAEsDBLMQj4GbDGzP476VBe6yxdXAVSZ4MklcfX7wM+TnhmOh+YEk9rXWeJupwCPBNb57mI68WkP3ZEeO6XXGdZ/7c0sxlmNsTMhhJ+Tz1jZueT5/rqsvI9cqgrbYTRaS8Tnodcm8c4hhFGFi4HViViITxHeBp4BfgtMCAHscwhdMPtJTznuCRdHIRRfTNj/a0ExuY4rrvjfVcQfjEMTjr/2hjXS8CZWYzro4Su1BXAsrhNzHedtRFXIdTZ8cDSGEMN8K2k/wcLCYOGfg2UxfJecX9tPD4sx3E9E+usBvglzSNgc/bznxTjOJpHt+a1vrrq5tPSOeecc2l4d6tzzjmXhidJ55xzLg1Pks4551waniSdc865NDxJOuecc2l4knQFSVL9gc9qOnecpI9kM54D3P9KSRd24H09JT2X9AHvvJL0uKQdiVUjksrTrR7xMUlLJL0naUrqqzrXtXmSdN3BOCAvSTImuM8TVlvIiIWJ8p8GPtPZcXXQD4ALUpTfAPzIzIYDbxM+cwrwOvA5OvC9O9dVeJJ0XYakSbFFs1TSbyVVxsm4vwRcpbB239/FmVB+I2lR3E6L778uTtK9QNI6SVckXfvCOCH1ckl3S+qnsCZgaTx+cPJ+kvHAEosTR8drj42vK+LUYEg6RmHtwWXxPiPi++cB57fje6+X9AOFdQt/K+nkpO/j7HjOUEm/i627JYnWtaRzJD0dZ3oZLOllSVWt72FmTwN1re6bdvUIM1tvZitonkQ7VdxD1XJNz69Jui6+vkJh/coVku49UB04lw8F0c3jXDv9HviwmZmkLwDXmNlXJd0G1JvZjQCS7iG0fH4v6UjgCeDoeI2jgL8nrJn4kqRbgQ8C3wQ+YmbbJA0wszpJCwjLDc0jTO91v5ntbRXTaUB7JpL+EnCTmc2O3ZU9YnkNYSLsA+lDmC7s65IeAP6DMA3aKELiepAw3+vHzawhJuE5hFldHpD0KeAyYALw72a2uR33hLZXj/hbTQeqzWx3Yno35wqNJ0nXlQwB5sa5MXsCr6U57x+AUWpeEu9ghdUtAB6xMPH0bklbCUtSjQd+bWbbAMwssQ7lHYTlhuYBFwOXprjXYMI8ogfyR+BahXX+7jezV+K9GiXtkdTPwjqO6ewBHo+vVwK7zWyvpJXA0FheCtwiaTTQSEj+CV8hJOQ/mdmcdsSbCyuA2ZLmEerYuYLj3a2uK/kxcIuZHQd8kTDnZColhBbn6Lgdbs2L4+5OOq+RNv5QNLPngaGSxgE9zKwmxWl/bRXHezT/v2oqN7N7gLPj+Y9KGp/0njKgIV0c0V5rnkNyX+L7sLA6R+J7uArYApwAjCX8IZEwJL6vUlIm/+/fIv3qEe2RXB/Qsq7OIsxlOgZYVCgDmJxL5knSdSX9af4FfVFSeR2h+zThSULLCYDYsmrLM8CnJQ2M5w9IOnYXYWDKz9O8dw0wPGl/PXBifN004lPSMGCdmd1MWH3h+Fg+ENiW6MaV9OIBYm1Lf6A2Js4LiF26MfnMAs6L8V7d3gvGxJxu9Yj22AIcKmmgwuLDn4gxlQBHmNl8wnqL/YG+6S/jXH54knSFqrekDUnb1cB1wK8lvQBsSzr3IeCcxMAd4ApgbBwQsprwPDAtM1sFfA94VtJyIHkJq9nAIYTne6k8BiQveH0j8GVJS4GKpPJ/BmoUVrE/lpB8ITwffQTCQB/CShEd9RPgovg9HAW8G8u/AfzOzH5PSJBfkHR06zdL+h1hNYjTY52fEQ9NA66WtJbwjPJn8fyTJG0APg38VNKq1teMyf87hNUlngISfwT0AH4Zu4uXAjdbWJPRuYLiq4A41waFz/9NNrNUH41InPMAYRDRKx24/v3AdDN7WWFF+WGxtemcKwCeJJ1LQ9KPgTOBiWb2chvnjQQqzey5DK/fEzjXzO464MnOubzwJOmcc86l4c8knXPOuTQ8STrnnHNpeJJ0zjnn0vAk6ZxzzqXhSdI555xL4/8BZ//GFL8RZhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7c2d8a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnplt = 0\n",
    "mxplt = 400\n",
    "plt.semilogy(latency_rt.index[mnplt:mxplt], latency_rt.core1[mnplt:mxplt], 'r', label='core1')\n",
    "plt.semilogy(latency_rt.index[mnplt:mxplt], latency_rt.core2[mnplt:mxplt], 'b', label='core2')\n",
    "plt.semilogy(latency_rt.index[mnplt:mxplt], latency_rt.core3[mnplt:mxplt], 'g', label='core3')\n",
    "plt.semilogy(latency_rt.index[mnplt:mxplt], latency_rt.core4[mnplt:mxplt], 'k', label='core4')\n",
    "#plt.title('Latency using Preempt-RT Raspbian Kernel ' + model + ' (4.14.52-rt34-v7+)')\n",
    "plt.title('Latency using Xenomai Patched Kernel ' + model + ' (4.14.37-v7+)')\n",
    "plt.ylabel('Number of latency samples')\n",
    "plt.xlabel('Latency (us), max %i us' % max_latency_rt)\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mnplt = 0\n",
    "#mxplt = 400\n",
    "#plt.semilogy(latency_rt_docker.index[mnplt:mxplt], latency_rt_docker.core1[mnplt:mxplt], 'r', label='core1')\n",
    "#plt.semilogy(latency_rt_docker.index[mnplt:mxplt], latency_rt_docker.core2[mnplt:mxplt], 'b', label='core2')\n",
    "#plt.semilogy(latency_rt_docker.index[mnplt:mxplt], latency_rt_docker.core3[mnplt:mxplt], 'g', label='core3')\n",
    "#plt.semilogy(latency_rt_docker.index[mnplt:mxplt], latency_rt_docker.core4[mnplt:mxplt], 'k', label='core4')\n",
    "#plt.title('Latency using Docker on RT-Preempt Raspbian Kernel (4.14.21-rt17-v7+)')\n",
    "#plt.ylabel('Number of latency samples')\n",
    "#plt.xlabel('Latency (us), max %i us' % max_latency_rt_docker)\n",
    "#plt.legend()\n",
    "#plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>core1</th>\n",
       "      <th>core2</th>\n",
       "      <th>core3</th>\n",
       "      <th>core4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27009553</td>\n",
       "      <td>25428764</td>\n",
       "      <td>1719841</td>\n",
       "      <td>13587554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13505516</td>\n",
       "      <td>22108741</td>\n",
       "      <td>44890727</td>\n",
       "      <td>35380040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1847548</td>\n",
       "      <td>36094</td>\n",
       "      <td>1554183</td>\n",
       "      <td>26420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1817965</td>\n",
       "      <td>697</td>\n",
       "      <td>800673</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1709953</td>\n",
       "      <td>2163968</td>\n",
       "      <td>997448</td>\n",
       "      <td>886626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         core1     core2     core3     core4\n",
       "time                                        \n",
       "13    27009553  25428764   1719841  13587554\n",
       "14    13505516  22108741  44890727  35380040\n",
       "11     1847548     36094   1554183     26420\n",
       "10     1817965       697    800673      1076\n",
       "15     1709953   2163968    997448    886626"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency_std.sort_values('core1', ascending=False, inplace=False, kind='quicksort').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>core1</th>\n",
       "      <th>core2</th>\n",
       "      <th>core3</th>\n",
       "      <th>core4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7902259</td>\n",
       "      <td>19388560</td>\n",
       "      <td>6138428</td>\n",
       "      <td>1535938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7349745</td>\n",
       "      <td>3574712</td>\n",
       "      <td>11559149</td>\n",
       "      <td>16301216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6562232</td>\n",
       "      <td>5577351</td>\n",
       "      <td>7066447</td>\n",
       "      <td>3517822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1699142</td>\n",
       "      <td>53670</td>\n",
       "      <td>54200</td>\n",
       "      <td>44115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1522800</td>\n",
       "      <td>1796940</td>\n",
       "      <td>1333736</td>\n",
       "      <td>2121763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        core1     core2     core3     core4\n",
       "time                                       \n",
       "18    7902259  19388560   6138428   1535938\n",
       "19    7349745   3574712  11559149  16301216\n",
       "15    6562232   5577351   7066447   3517822\n",
       "43    1699142     53670     54200     44115\n",
       "14    1522800   1796940   1333736   2121763"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency_rt.sort_values('core1', ascending=False, inplace=False, kind='quicksort').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#latency_rt_docker.sort_values('core1', ascending=False, inplace=False, kind='quicksort').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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