derkling/20170623_SchedUtil_FlagsFixes_Report.ipynb

## 20170623_SchedUtil_FlagsFixes_Report.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Report on SchedUtil Flags Fixes"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Experiments Setup"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The following results have been generated by running **synthetic RTApp workloads** on a **big cluster (CPU1 and CPU2)** of a JUNO R2 board.<br>\nThe systems has been configured to isolate the CPUs of the big cluster using CGroups to move all existing tasks in the other available CPUs.<br>\nThus, CPU1 and CPU2, which shares the same frequency domain, are used to run just the synthetic workload generated by the following tests.\n\nThe following experiments show results for these kernel configurations:\n\n- **base** tip/sched/core\n- **ignore_sugov**: base + patches to fix small FAIR task running at MAX OPP\n- **rt_mode**: ignore_sugov + patch to fix FAIR wakeup reducing the OPP of the current RT task\n- **full_stack**: the complete patch set applied\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Experiments Results"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Case A: Small FAIR task running at MAX OPP"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "TRACE_TEST = 'fair_small'",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This test is configured to run a single small RTApp task pinned on CPU2 while the sugov RT thread is running on CPU1.<br>\n**rta_fr** is a FAIR task running **1ms every 10ms**. For such a small task we expect the OPP of the big cluster to alwasy be the minimum."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Cluster Frequencies"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Plot1:** base kernel: CPU2 frequency keep jumping between MAX and MIN OPP.<br>\nThe frequency jump is due to the sugov RT task being activated on CPU1, however the only other task active in the system is a small FAIR task, thus we would expect to run most of the time at the lower OPP.\n\n**Plot2,3,4:** {fll_stack,ignore_sugov,rt_mode} kernel: CPU2 frequency is almost always at minimum OPP.<br>\nThe few times when we have a jump to MAX OPP are due to the watchdog (RT) task waking up on a CPU of the big cluster."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plotFrequencies(trace_df, cpus=2, task='rta_fr', tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST)",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:49,385 INFO    : ('fair_small', 'dft_rl500', 'base')\n2017-06-23 14:27:49,564 INFO    : CPU02 average frequency: 1.100 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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CdZJ0ctj6W+MdwMw2SNogSTfF2V5TU6PaHTtUUFGhxXG2N7e06Pju3WrKyNDK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\n3NdZXp46Dx3ydVZYqK7KShV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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c82e11e10>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:49,766 INFO    : ('fair_small', 'dft_rl500', 'full_stack')\n2017-06-23 14:27:49,927 INFO    : CPU02 average frequency: 0.944 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gbqj1pAXBmlK/WVPqN2tK/WQ9aRiGfZXmO5ulyjTPdzXtm4AjesYd3rRN1y5JkiRJ0oyG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\n+s2aUr9ZU+on60k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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c834ad810>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:50,106 INFO    : ('fair_small', 'dft_rl500', 'ignore_sugov')\n2017-06-23 14:27:50,272 INFO    : CPU02 average frequency: 0.835 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c834e0550>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:50,454 INFO    : ('fair_small', 'dft_rl500', 'rt_mode')\n2017-06-23 14:27:50,621 INFO    : CPU02 average frequency: 0.866 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c82f56890>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Tasks RunTimes"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A small FAIR RTApp task is expected to run longher then its configured runtime when the CPU is not at its maximum OPP.<br>\nHowever, **Plot1** shows that with the current tip/sched/core such a task is still able to complete more than 50% of its activations with the 1ms threshold, which corresponds to its configured running time (1ms every 10ms).\n\nFollowing plots shows that less than 2% activations complete within the 1ms threshold. This match with the average frequency being in general lower with the patches applied."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "s = plotTaskRuntimes(trace_df, tasks=['rta_fr'], threshold_ms=1, tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST, check_cosheduling=True)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:50,811 INFO    : ('fair_small', 'dft_rl500', 'base')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:51,439 INFO    : Found:   301 activations for [2016: rta_fr]\n2017-06-23 14:27:51,443 INFO    : 51.2 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h6fu2l9s+ut+FQXGm9rsAwygibrL9SUnfl/R7SddKery/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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8563bed0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:52,198 INFO    : ('fair_small', 'dft_rl500', 'full_stack')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:52,447 INFO    : Found:   301 activations for [2026: rta_fr]\n2017-06-23 14:27:52,448 WARNING : Discarding 9 running times (above 2 x threshold_ms, 3.0% of the overall activations)\n2017-06-23 14:27:52,450 INFO    : 0.7 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAACAUiPwBQAAAACUGoEvAAAAAKDUCHwBAAAAAKVG4AsAAAAAKLVC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\nzGGPARF10oGo/tTT83fqmU8eibxM3XWGeo5vHKkXQ2E9NfKq9YWIqIq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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c80ed1550>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:53,206 INFO    : ('fair_small', 'dft_rl500', 'ignore_sugov')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:53,454 INFO    : Found:   301 activations for [2019: rta_fr]\n2017-06-23 14:27:53,455 WARNING : Discarding 9 running times (above 2 x threshold_ms, 3.0% of the overall activations)\n2017-06-23 14:27:53,457 INFO    : 0.0 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAKDQCHwBAAAAAIVG4AsAAAAAKDQCXwAAAABAoRH4AgAAAAAKjcAX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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c80bc3ad0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:54,219 INFO    : ('fair_small', 'dft_rl500', 'rt_mode')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:54,474 INFO    : Found:   301 activations for [2033: rta_fr]\n2017-06-23 14:27:54,475 WARNING : Discarding 8 running times (above 2 x threshold_ms, 2.7% of the overall activations)\n2017-06-23 14:27:54,478 INFO    : 1.4 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PdrRgBUhd7T+l7CdfJihbxh9KOiUiimiBxgjm7HeZZyp7WdiWylrhL5b0/yL7CSUA6HkE\nuADQ5VJX4h1qjPrXiBjwt3LLyPbRyp4ZfImyVs4TIuInnS0VAADoBgS4AAAAAIBS4CVTAAAAAIBS\naCrAtb2/7dttL7X9op/fsL2B7YvT+OttT8yNOyENv932fmnYdrZ/aftW20tsfzyXfnPbP7N9R/r/\n0qEvJgAAAACg7Bp2UU6/5/cnSW9R9jKCGyUdFhG35tJ8RNnbCo+xfaikf4yI99ierOxV/FMlbSPp\n58p+2P5lkraOiJtsbyxpoaR3RsSttr8q6bGIOCUF0y+NiM8MVMYtttgiJk6cOJjlBwAAAAB0uYUL\nFz4aEeMbpRvTRF5TJS2NiLskyfYcSQcr+53GioOV/YaklP0G4Jm2nYbPiYjnJN1te6mkqRFxnaQH\nJSkiVtq+TdnvTN6appmR8vqmpPmSBgxwJ06cqAULFjSxKAAAAACAXmP73mbSNdNFeVtJ9+W+L0vD\naqaJiNWSnpQ0rplpU3fm1yj7gXpJ2jL3Uw8PKXuN/YvYPtr2AtsLli9f3sRiAAAAAADKrKMvmbK9\nkaRLJX0iIp6qHh9Z/+mafagj4pyI6IuIvvHjG7ZUAwAAAABKrpkA935J2+W+T0jDaqaxPUbSppJW\nDDSt7fWUBbffiYjv59I8bHvrlGZrSY80uzAAAAAAgJGrmQD3Rkk72Z5ke31Jh0qaV5VmnqQj0udD\nJF2dWl/nSTo0vWV5kqSdJN2Qns89T9JtEfG1AfI6QtKPWl0oAAAAAMDI0/AlUxGx2vaxkq6UNFrS\n+RGxxPZJkhZExDxlweq300ukHlMWBCulu0TZy6NWS/poRDxv+42S3i9pse1FaVafjYjLJZ0i6RLb\nR0m6V9K7i1xgAAAAAEA5NfyZoF7Q19cXvEUZAAAAAMrJ9sKI6GuUrqMvmQIAAAAAoCgEuD2uf35/\np4sAAAAAAF2BALfHzb5mdqeLAAAAgBbQQAG0DwEuOoaDO4CRguMdgDwaKHoHx+/eQ4Dbg/rn98uz\nLc+2JK353Gs7IAf33tdrdQ7tQ10YGMc7AOhNHL97DwFuD+qf0a+YFYpZ2RuwK5/7Z/R3tmAYcTjo\no4K6gLLgZg0qiq4LZWmgALodAS6GFQd3tBP1CIPVjrrD8a43cbMGFUXXBRooegfH7+4xmHXO7+D2\nuP75/T17YPRsrznIN6OXl7VM+uf31zzpz5o+q+Pbp9U6haEZSl3otv253XWHutk72FaoaGddoJ71\nDrZVZ+XXP7+DWzL17l500wViu7V6J5W7bO3Ri3egqQvtMZS6QCtZ9xnJ+0nRrTUjeV32uuFquZs1\nfVah+bVL3evPLqvj3VYedBYBboe0uiO2cjHY6sGo1bIUdRAp6uBerzxFXUBz0CxuHXSqGyjBFGoZ\nzi5ovXAxO5L3k6Jv3I3kdZnXi+fP4bqJ2803hfPq1eV6wzu1zdu5z7X7erXd66zb5tvstLXOz9pE\n2zSVQUT0/N+ee+4ZvUb9qjl81i9ntZS+lbStDi+iLAPl00z6Wb+cFerXi/7yaVpdrlYVlU8va7WO\ntJpPUdpdF1pd3nYayn41lHyGkn8z+3NR821VN+3nw7lN8tq9Doqqg+3eD4tYD51al92m1XNHty1X\nrfL3StmLUtQ1ZVHaefzq1LGl3dfVRV3/F3VOGUr5JS2IJmLDjgenRfyVKcDNDx/sxWBRB6NOBcRD\nKU8z66zbLvq6SbtvahSVTzP5D2ddKOKk2O6ytPsEV0+7T+jtvpFV1Im+lbRFlaVTx8yi6mA97d5W\ngx3ejhs49RR1LGr3umn39UW7b47USl9U2VuZ52DyGUr+9ery9P+dPuhGh3bUqaEcvxrl3YxeOQZ2\nKi5ox/IS4HahdrZGtnowavUgNZwtqUVdlHUqUO5lra6DovJpd1DWqYN1Kye/bjvRDOf+nNfuC4NO\nXaC3M++yDu/U/lxPK/kM50V+M8Prace6bPf5uZnh7bxJ2U31LKJzx7Ru2+btPCa3mr6ZddPMdXg7\nbl4O5SZFu+tCPetMS4Db3dpZGbrhINVqcDScQVCnLo56QTdfNHWqPMN14m5HWYbzBJfX7v25qJsv\nzRiui6l2H3vbfVHT7vIM5wV0PUXvz60Ob8eF43Aeq7utbjYzvJ1lr6dTy9rNwzvRQDSU80mvrIOh\n7J/tOIY3o9kAl58J6pB6rxyvN7yVn9RoNe+hDG/mZ0Jafb16Uenz66xeOafvMF3X3HvNi4Y3U/6y\nvDa+qG04lHyaqVPNbKuBylZ0XShiebupLAPlM5R1P5T9uVPHl1bXQyvrp9VlGs5j11D2k3aUpx11\nttW6PNj1X++c3anz8FCWqVPbvBuOj7V0wzYZzmN7vbrcTB0fzuNIM8evRuesdpwfOnUMbPd82zG8\nGc3+TFBHW16L+uvFFtxWWy+KyLuobiyd6jJVT8t3f1osZzPPpZSlu3Kr23wo+Qxnt/dWyzmcd0Er\ny9uOsgzn8HqaSd/uutANd+jbmXentuFw7j/D2ZpXz2DzaXfLyHAu01DOn+2eb1E9XBodF7qhnnWq\nd04zeuXRgnbm3er+0O5ro051Y29HrCO6KPembgqU2hHUtGO+rerEQbNXFLVMnbrIalU3Xay1uyyd\nesZvOG9ADSX/ospTxMVUu59Jb/c2aXcd7MTz0kXl020Xjp0KOrrhudFmhtdSVNnr6dSytuOaph03\nptp5zG/3sbFVwxk4NpN/UeeUoVznE+Bi2HVTcD6Qdl/09bKitmG7b460u5xFHaxbOfl1w4mjmXw6\ndQOqqPm2+0K8lXIWtW3bvU3afbFTTzdtq6Ly6VTg2Gr6Tm3zejp1c6QI7d4mnbrx1YzqVudOlKed\ndbNXjoFlQoDbQ4qokL1yUdtt5RxK+nqfy2I4L1iamVe3BbutlrmI+tJtF531tGOdFVWe4VwntebV\nbduqnm7oedMNx4VW5lvUMaGb6utgytAN5/mizu2DzbMbzp/dcExpd5DdDctY0Y7tMJRjQadu7LW9\n4YAAt3cUcQDolW6JnSpnu+/ylrE1t6ht1al13+46VVS3s1YulDvVVbSebts/i8qnniLm2+5t1W37\nZz3tLme7t3kr27ZTx7ReOa922/opoudNp7psdyroqA7E1D88z5C2cz10W33t1PGl3V3gW54vAW7v\nKKKSdNuzGd32DEm3rZ9e0G3rsleG19ML+3O3nfjKuLzdtqy9sD8Mx/Aitnm3LRPD2z+c/aG8w7up\nLAzPDW8ywB01qHc0Y8j65/fLsy3PtiSt+dw/v39Y8xgonxkXzGhr/p0qZ6vlaff66SZFbaui8u9U\n3ayXf7vrWifK2IxaP0Ew1HK2c50NNZ9mlrfV+fbCMaSo/bPd5W/HcWEodbybjmmt6pX9tt3rp4j5\ndtv5cyiGcgysp9vqWhG67fq53eu41euOTm7bMS1PgUL0z+iv+3ttlQ1bGSe9+Pe1KsMrv3M11Dwq\n0+bzyas3fJ1lmr/ub3E1yr/d5ayXf6vlKWr99IKi6mWr27CodT+cdb+pfaLF9Vmdf5FlHOyx5UXL\n1OJ+3mp52n28GGi+rSzvYOtUO7ZVUcPbsX/mtbouh+NcUGQd74ZjWifXZTed59u5Hir1o1JHhmt/\nKGodF3XMbyafWdNnrc2nzvkwbzjrWr3h1eVptKyduEZp9/GlHddG7TinDKiZZt5u/6OLcjF5DGZ4\nPZ0qT7cN72Xdti57ZXg9vbA/54e34zmqbh4+XMvbDcs6nMPr6YZyFr3Nu2GZGD68w9kfBh5eTzcs\nb6Ph3VQWhueGN9lFeczgQ2MUJX+Hq5N5DJRPu/MvKp9W8y8qfVHL1U3avUzdtu5bzb/dda2Isgxl\nns3cba+nm9ZZs/kMZXlbmW+vHEM6ta1aNZT1WXQd77ZjWqt6Zb/t1Lmplfl22/mzGe04BtbTbXWt\nCN12/dzuddzt10yj+/v7h5RBNzjnnHP6jz766E4XY9BmTJzR8rjq4UXk0WraRlrJv9V5D6acRZSn\nyPXT7YraVvWGD8e6b2edanWbF7G8RZaxlWlmXzO7Zte1euk7tX8WlU+ry9vKfNu9rYoaXuS2aiWf\nVocXtT6LqOPddkxrdXiv7LedOjcVtf/U0879s9XhRR0D6+m2utZq/t10jdLu8rT7/NPqfGfPnv1g\nf3//OXUzS5y19va2vr6+WLBgQaeLAQCl1D+/v+7FThmNtOUF2xzIY39At7K9MCL6GqYjwAUAAAAA\ndLNmA9xRw1EYAAAAAADajQAXGOF6+Xd7AQAAgDwCXGCEa8cPugMAAACdQIALAAAAACgFAlxgBOqf\n3y/PtjzbkrTmM92VAQAA0Mt4izIwwrX7B90BAACAoeItygAAAACAEYUAFxjhZk2f1ekiAAAAAIUg\nwAVGuP4Z/Z0uAgAAAFAIAlwAAAAAQCk0FeDa3t/27baX2p5ZY/wGti9O46+3PTE37oQ0/Hbb++WG\nn2/7Edu3VOU1xfbvbC+yvcD21MEvHgAAAABgpGgY4NoeLeksSQdImizpMNuTq5IdJenxiNhR0qmS\nvpKmnSzpUEm7SNpf0tkpP0m6IA2r9lVJsyNiiqQvpO8AAAAAAAyomRbcqZKWRsRdEfE3SXMkHVyV\n5mBJ30yf50rax7bT8DkR8VxE3C1pacpPEfErSY/VmF9I2iR93lTSAy0sDwAAAABghBrTRJptJd2X\n+75M0uvrpYmI1baflDQuDf9d1bTbNpjfJyRdafs/lQXge9VKZPtoSUdL0vbbb9/EYgAAAAAAyqwb\nXzL1b5I+GRHbSfqkpPNqJYqIcyKiLyL6xo8fP6wFBAAAAAB0n2YC3PslbZf7PiENq5nG9hhlXYtX\nNDlttSMkfT99/p5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X9VMkvUbSCR0uz5DZXl/Zs2q7SNpf0iaS3iBphaSpuaTv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\nUUQ8ABwCm558sAi4HHgv8JWI+PJwt2FmZtZKpZfy+YrvvJq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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c80b961d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Veriation of collected metrics with respect to the base kernel"
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "variations_df = plotDeltas(results_df,\n                           ('fair_small', 'dft_rl500', 'base'),\n                           threshold_pct=25)\nvariations_df",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:45:15,378 INFO    : Deltas wrt: ('fair_small', 'dft_rl500', 'base')\n2017-06-23 14:45:15,552 INFO    : Limiting x axis to 25[%] variations\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 77,
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">fair_small</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">dft_rl500</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>delta_full_stack</th>\n      <th>delta_ignore_sugov</th>\n      <th>delta_rt_mode</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>energy</th>\n      <td>-24.269689</td>\n      <td>-53.223704</td>\n      <td>-52.738186</td>\n    </tr>\n    <tr>\n      <th>freq_avg_cpu2</th>\n      <td>-14.157060</td>\n      <td>-24.047165</td>\n      <td>-21.296450</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_fr_99p</th>\n      <td>45.786726</td>\n      <td>47.129008</td>\n      <td>45.357196</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_fr_avg</th>\n      <td>73.348250</td>\n      <td>73.270555</td>\n      <td>68.622183</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_fr_max</th>\n      <td>39.007092</td>\n      <td>41.276596</td>\n      <td>38.936170</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_fr_min</th>\n      <td>63.870968</td>\n      <td>91.612903</td>\n      <td>509.032258</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_fr_std</th>\n      <td>14.405392</td>\n      <td>15.541383</td>\n      <td>57.287317</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                         fair_small                                 \n                          dft_rl500                                 \n                   delta_full_stack delta_ignore_sugov delta_rt_mode\nenergy                   -24.269689         -53.223704    -52.738186\nfreq_avg_cpu2            -14.157060         -24.047165    -21.296450\nruntime_rta_fr_99p        45.786726          47.129008     45.357196\nruntime_rta_fr_avg        73.348250          73.270555     68.622183\nruntime_rta_fr_max        39.007092          41.276596     38.936170\nruntime_rta_fr_min        63.870968          91.612903    509.032258\nruntime_rta_fr_std        14.405392          15.541383     57.287317"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d24n1XtRD/TuBLaaxv2uBrZPsC5BkoySTudvevf8tgJuSbERz534mjSTytyWZ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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c805a7a50>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Case B) FAIR wakeup reducing the OPP of the current RT task"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "TRACE_TEST = 'fair_wakeup'",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This test is configured to run a small RTApp task, pinned on CPU2, in such a way to periodically wakeups on a CPU where another RT task is currently running.<br>\n\n**rta_rt** is a RT task running **8ms every 16ms**.<br>\n**rta_fr** is a FAIR task running **1.6ms every 16ms**, which wakes up **3ms after each rta_rt activation**\n\nFor such a scenario we would like that the RT task execution is not affected by the FAIR task wakeup.<br>\nThe RT task should always be able to run at the maximum OPP thus completing its activations within the configured 8ms threshold."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Cluster Frequencies"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Plot1:** base kernel: CPU2 frequency keep jumping between MAX and MIN OPP.<br>\nThe frequency jump is due to the FAIR task waking up on CPU2 and resetting the flag set by the currently running RT task.<br>\nHowever would expect the OPP to be high much more time.\n\n**Plot2,3,4:** {fll_stack,ignore_sugov,rt_mode} kernel: CPU2 frequency has more chances to be at the highest OPP.<br>\nThe few times when we have a jump to the MIN OPP are due to the FAIR task being able to reduce the frequency before entering sleep."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "plotFrequencies(trace_df, cpus=2, task='rta_rt', tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST)",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:55,237 INFO    : ('fair_wakeup', 'dft_rl500', 'base')\n2017-06-23 14:27:55,399 INFO    : CPU02 average frequency: 1.100 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9+yx1W7YMW/7cNWs4d5jyh2v/fevWUdvZeVD9Y19LC9vXrWP3MOW3jaL/Hblh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\nK0mSJEnSsCZlwZtSWgksLbPr0jLHJuA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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81577050>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:55,602 INFO    : ('fair_wakeup', 'dft_rl500', 'full_stack')\n2017-06-23 14:27:55,755 INFO    : CPU02 average frequency: 0.972 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8563b6d0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:55,942 INFO    : ('fair_wakeup', 'dft_rl500', 'ignore_sugov')\n2017-06-23 14:27:56,093 INFO    : CPU02 average frequency: 0.982 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81577790>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:56,287 INFO    : ('fair_wakeup', 'dft_rl500', 'rt_mode')\n2017-06-23 14:27:56,442 INFO    : CPU02 average frequency: 0.945 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81a64c90>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Tasks RunTimes"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A RT RTApp task is expected to complete most of its activations within the configured runtime since the CPU should be at the maximum OPP.<br>\nHowever, **Plot1** shows that with the current tip/sched/core such a task is able to complete less than 4% of its activations with the 8ms threshold, which corresponds to its configured running time (8ms every 16ms).\n\nFollowing plots shows that more than 70% activations complete within the 8ms threshold, and up to 98% with the full stack<br>\nThis match with the average frequency being in general higher while a RT task is running with the proposed patches applied."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plotTaskRuntimes(trace_df, tasks=['rta_rt'], threshold_ms=8, tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST, check_cosheduling=False)",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:56,644 INFO    : ('fair_wakeup', 'dft_rl500', 'base')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:56,896 INFO    : Found:   189 activations for [2207: rta_rt]\n2017-06-23 14:27:56,899 INFO    : 3.7 % samples below 8 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cwXx3Vb1loeNZ7JL8G81Dfr5dVZvVz1i0idT7qqr/ksxFrb2f+2vAo9oH7ZDkL2mS4WNp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\n08hdB8xshOx+bO2W3TtPfNUoR2Jj3Vj8rqmdLkuVtA64vdlxjKAd2MT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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c834e0510>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:57,697 INFO    : ('fair_wakeup', 'dft_rl500', 'full_stack')\n2017-06-23 14:27:57,886 INFO    : Found:   188 activations for [2217: rta_rt]\n2017-06-23 14:27:57,889 INFO    : 98.4 % samples below 8 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tj3Mm+V2antQ/pxns6EaansbVwIeq6sOLU9qlLcm5wAsm3ttN8hya4GzG4HFrkuTPaG6e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\nAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81d475d0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:58,760 INFO    : ('fair_wakeup', 'dft_rl500', 'ignore_sugov')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:58,981 INFO    : Found:   188 activations for [2210: rta_rt]\n2017-06-23 14:27:58,983 INFO    : 78.2 % samples below 8 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P6/PXf37d9lQqze/NyImlGGrVv+PoHTVhywlV4y/FVS3hKi1Xzaz5szbYr/Vi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\n35ocRPPkJ+kAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81160f90>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:59,740 INFO    : ('fair_wakeup', 'dft_rl500', 'rt_mode')\n2017-06-23 14:27:59,937 INFO    : Found:   187 activations for [2224: rta_rt]\n2017-06-23 14:27:59,939 WARNING : Discarding 1 running times (above 2 x threshold_ms, 0.5% of the overall activations)\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:59,941 INFO    : 97.3 % samples below 8 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aVVdddfnd0x122EFXXnmlJOn666/XJZdcIkn62Mc+piOPPLLp5dhz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\nJPUka2QlSY0ZHw9HLJ6lzHy05bYxMy+m1La2FRGvAB7KzBt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W+OdY148hIlZSLkp9FCAz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\n6iv/HxJXcHVIB0saAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c818471d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Veriation of collected metrics with respect to the base kernel"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "variations_df = plotDeltas(results_df,\n                           ('fair_wakeup', 'dft_rl500', 'base'),\n                           threshold_pct=25)\nvariations_df",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 15:31:33,689 INFO    : Deltas wrt: ('fair_wakeup', 'dft_rl500', 'base')\n2017-06-23 15:31:33,976 INFO    : Limiting x axis to 25[%] variations\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 81,
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">fair_wakeup</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">dft_rl500</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>delta_full_stack</th>\n      <th>delta_ignore_sugov</th>\n      <th>delta_rt_mode</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>energy</th>\n      <td>-1.809203</td>\n      <td>-4.908744</td>\n      <td>-9.824553</td>\n    </tr>\n    <tr>\n      <th>freq_avg_cpu2</th>\n      <td>-11.627556</td>\n      <td>-10.662387</td>\n      <td>-14.041744</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_99p</th>\n      <td>-6.600190</td>\n      <td>0.987271</td>\n      <td>-2.826452</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_avg</th>\n      <td>-7.890454</td>\n      <td>-3.650208</td>\n      <td>-7.547424</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_max</th>\n      <td>-7.322270</td>\n      <td>-7.244786</td>\n      <td>-45.954672</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_min</th>\n      <td>75.824176</td>\n      <td>80.219780</td>\n      <td>74.725275</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_std</th>\n      <td>-24.225188</td>\n      <td>-25.748334</td>\n      <td>-41.317060</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                        fair_wakeup                                 \n                          dft_rl500                                 \n                   delta_full_stack delta_ignore_sugov delta_rt_mode\nenergy                    -1.809203          -4.908744     -9.824553\nfreq_avg_cpu2            -11.627556         -10.662387    -14.041744\nruntime_rta_rt_99p        -6.600190           0.987271     -2.826452\nruntime_rta_rt_avg        -7.890454          -3.650208     -7.547424\nruntime_rta_rt_max        -7.322270          -7.244786    -45.954672\nruntime_rta_rt_min        75.824176          80.219780     74.725275\nruntime_rta_rt_std       -24.225188         -25.748334    -41.317060"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3mexs+Tcm2bGdWO/gHsrfDmw6ifNdDWyZZA+AJBsk2Xkcx3eff1PghiQb0IyH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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8142dd50>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Case C) RT wakeup not running at max OPP"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "TRACE_TEST = 'rt_wakeup'",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This test is configured to run a small RT task, pinned on CPU2, in such a way to periodically preempt another FAIR task running on the same CPU.<br>\n\n**rta_fr** is a FAIR task running **4ms every 16ms.****<br>\n**rta_rt** is a RT task running **1.6ms every 16ms**, which wakes up **4ms after each rta_fr activation.<br>\n\n\nFor such a scenario we would like that the RT task execution is not affected by the FAIR task already running on the same CPU.<br>\nThe RT task should always be able to run at the maximum OPP thus completing its activations within the configured 8ms threshold."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Cluster Frequencies"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Plot1:** base kernel: CPU2 frequency keep jumping between MAX and MIN OPP.<br>\nThe frequency jump is (hopefully) due to the RT task waking up on CPU2 and setting the flag to jump to the maximum OPP.<br>\n\n**Plot2,3,4:** {fll_stack,ignore_sugov,rt_mode} kernel: there are not noticeable differences with respect to the base case<br>"
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "plotFrequencies(trace_df, cpus=2, task='rta_rt', tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:00,702 INFO    : ('rt_wakeup', 'dft_rl5000', 'base')\n2017-06-23 14:28:00,872 INFO    : CPU02 average frequency: 1.092 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81a64c50>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:01,065 INFO    : ('rt_wakeup', 'dft_rl5000', 'full_stack')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:01,303 INFO    : CPU02 average frequency: 1.036 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c83563ed0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:01,517 INFO    : ('rt_wakeup', 'dft_rl5000', 'ignore_sugov')\n2017-06-23 14:28:01,691 INFO    : CPU02 average frequency: 1.003 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81e12a50>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:01,970 INFO    : ('rt_wakeup', 'dft_rl5000', 'rt_mode')\n2017-06-23 14:28:02,159 INFO    : CPU02 average frequency: 0.992 GHz\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8142df90>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Tasks Runtimes"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A RT RTApp task is expected to complete most of its activations within the configured runtime since the CPU should be at the maximum OPP.<br>\nHowever, **Plot1** shows that with the current tip/sched/core such a task is almost never able to complete with the 1.6ms threshold, which corresponds to its configured running time (1.6ms every 16ms).\n\nFollowing plots shows that the current situation is not improved by the additional patches.<br>\nHowever, we assume that this is mainly due to the slow OPP changes on the target JUNO board used for the experiments.\nIndeed, on that target, it takes ~3ms to complete an OPP change, which is already higher compared to the configured threshold.\n\nStill, by looking at a trace, we can still notice an improved sequencing of kernel generated events.\nWith the complete series of patches, a RT task waking up on a CPU where an OPP transition is not already in progress has pretty god chances to trigger an OPP switch as soon as it enter the CPU. If the target platform has a relatively fast (compared to the RT task running time) CPUFreq driver, than we can ensure the task will run at the maximum frequency."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plotTaskRuntimes(trace_df, tasks=['rta_rt'], threshold_ms=1.6, tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST, check_cosheduling=False)",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:02,358 INFO    : ('rt_wakeup', 'dft_rl5000', 'base')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:02,558 INFO    : Found:   189 activations for [2007: rta_rt]\n2017-06-23 14:28:02,560 WARNING : Discarding 1 running times (above 2 x threshold_ms, 0.5% of the overall activations)\n2017-06-23 14:28:02,563 INFO    : 1.6 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ScknrJJ0YEc9JUrU8y2Y7V9JpFUU5QtLf2l4n6UlJc1NwDQAAAABA\nTS5y7Dg0NBQjIyO9LgYAAAAAoAtsL4mIoUbp+rlzKwAAAAAA2kbgCwAAAAAoNAJfAAAAAEChEfgC\nAAAAAAqNwBcAAAAAUGgEvgAAAACAQiPwBQAAAAAUGoEvAAAAAKDQCHwBAAAAAIVG4AsAAAAAKDQC\nXwAAAABAoRH4AgAAAAAKjcAXAAAAAFBoBL4AAAAAgEIj8AUAAAAAFBqBLwAAAACg0Ah8AQAAAACF\nRuALAAAAACg0Al8AAAAAQKER+AIAAAAACo3AFwAAAABQaAS+AAAAAIBCyxX42p5te4XtlbbnVRm/\nie3L0vibbE8tG3dyGr7C9sGN8rR9ke27bS9Nf3un4bZ9Vkr/K9uvb2fBAQAAAADjQ8PA1/YESWdL\nOkTSDElH2p5Rkew4SQ9HxK6SzpB0epp2hqS5knaXNFvSObYn5MjznyJi7/S3NA07RNL09He8pC+2\nssAAAAAAgPElzxPffSWtjIi7IuIZSQskzalIM0fSxenzQkkH2XYaviAino6IuyWtTPnlybPSHElf\niczPJW1le7sc5QcAAAAAjGN5At8dJN1b9n1VGlY1TUSsk/SIpK3rTNsoz8+k5sxn2N6kiXIAAAAA\nALCBfuzc6mRJr5b0F5JeIemTzUxs+3jbI7ZH1qxZ043yAQAAAAAGSJ7A9z5JO5Z9n5KGVU1je6Kk\nLSWtrTNtzTwjYnVqzvy0pC8raxadtxyKiPMiYigihiZPnpxj8QAAAAAARZYn8L1F0nTb02xvrKyz\nqkUVaRZJOiZ9PkLSdRERafjc1OvzNGUdU91cL8/R93bTO8KHS7q9bB5Hp96dZ0p6JCJWt7TUAAAA\nAIBxY2KjBBGxzvZJkq6RNEHShRGxzPapkkYiYpGkCyRdYnulpIeUBbJK6S6XtFzSOkknRsRzklQt\nzzTLr9meLMmSlko6IQ2/WtKhyjrIekLSB9peegAAAABA4Tl7MFtMQ0NDMTIy0utiAAAAAAC6wPaS\niBhqlK4fO7cCAAAAAKBjCHwBAAAAAIVG4AsAAAAAKDQCXwAAAABAoRH4AgAAAAAKjcAXAAAAAFBo\nBL4AAAAAgEIj8AUAAAAAFBqBLwAAAACg0Ah8AQAAAACFRuALAAAAACg0Al8AAAAAQKER+AIAAAAA\nCo3AFwAAAABQaAS+AAAAAIBCI/AFAAAAABQagS8AAAAAoNAIfAEAAAAAhUbgCwAAAAAoNAJfAAAA\nAEChEfgCAAAAAAqNwBcAAAAAUGi5Al/bs22vsL3S9rwq4zexfVkaf5PtqWXjTk7DV9g+uFGetr+W\nht9u+0Lbk9LwWbYfsb00/X2qnQUHAAAAAIwPDQNf2xMknS3pEEkzJB1pe0ZFsuMkPRwRu0o6Q9Lp\nadoZkuZK2l3SbEnn2J7QIM+vSXq1pD0kbSbpQ2XzuSEi9k5/p7aywAAAAACA8SXPE999Ja2MiLsi\n4hlJCyTNqUgzR9LF6fNCSQfZdhq+ICKejoi7Ja1M+dXMMyKujkTSzZKmtLeIAAAAAIDxLE/gu4Ok\ne8u+r0rDqqaJiHWSHpG0dZ1pG+aZmjgfJel7ZYPfYPtW29+1vXuOsgMAAAAAxrmJvS5AHedI+nFE\n3JC+/0LSzhHxuO1DJV0haXrlRLaPl3S8JO20005jVVYAAAAAQJ/K88T3Pkk7ln2fkoZVTWN7oqQt\nJa2tM23dPG3PlzRZ0j+MDouIRyPi8fT5akmTbG9TWdiIOC8ihiJiaPLkyTkWDwAAAABQZHkC31sk\nTbc9zfbGyjqrWlSRZpGkY9LnIyRdl97RXSRpbur1eZqyJ7Q318vT9ockHSzpyIh4fnQGtl+Z3huW\n7X1T2de2stAAAAAAgPGjYVPniFhn+yRJ10iaIOnCiFhm+1RJIxGxSNIFki6xvVLSQ8oCWaV0l0ta\nLmmdpBMj4jlJqpZnmuW5kn4r6cYU534r9eB8hKS/tb1O0pOS5qbgGgAAAACAmlzk2HFoaChGRkZ6\nXQwAAAAAQBfYXhIRQ43S5WnqDAAAAADAwCLwBQAAAAAUGoEvAAAAAKDQCHwBAAAAAIVG4AsAAAAA\nKLSGP2cEAEDRPfvss1q1apWeeuqpXhcFyaabbqopU6Zo0qRJvS4KAKAACHwBAOPeqlWrtMUWW2jq\n1KlKvyGPHooIrV27VqtWrdK0adN6XRwAQAHQ1BkAMO499dRT2nrrrQl6+4Rtbb311jyBBwB0DIEv\nAAASQW+fYXsAADqJwBcAAAAAUGgEvgAAtKi0uNTrIjT0oQ99SMuXL+9Yfvfcc4++/vWvr/8+MjKi\nj370ox3LHwCAbiDwBQCgRcPXD3c8z4jQ888/37H8zj//fM2YMaNj+VUGvkNDQzrrrLM6lj8AAN1A\n4AsAQI/dc8892m233XT00Ufrta99rSZMmLB+3MKFC3XsscdKko499lh99KMf1X777adddtlFCxcu\nlCQtXrxYs2bN0hFHHKFXv/rVet/73qeIkCTNmjVLIyMjkqTNN99cp5xyivbaay/NnDlTDzzwgCTp\nzjvv1MyZM7XHHnvoX/7lX7T55pvXLOu8efN0ww03aO+999YZZ5yhxYsX67DDDpMklUolHXPMMdp/\n//21884761vf+pY+8YlPaI899tDs2bP17LPPSpKWLFmiAw88UPvss48OPvhgrV69urMrFACACgS+\nAAA0obS4JA9bHs46Xxr93G6z59/85jf68Ic/rGXLlumlL31pzXSrV6/WT37yE1111VWaN2/e+uG/\n/OUvdeaZZ2r58uW666679NOf/vRF0/7pT3/SzJkzdeutt+qAAw7Ql770JUnSxz72MX3sYx/Tbbfd\npilTptQt52mnnab9999fS5cu1d///d+/aPydd96p6667TosWLdL73/9+velNb9Jtt92mzTbbTN/5\nznf07LPP6iMf+YgWLlyoJUuW6IMf/KBOOeWUvKsJAICW8Du+AAA0oTSrpNKskqQs6I350ZF8d955\nZ82cObNhusMPP1wbbbSRZsyYsf6JrSTtu+++64PWvffeW/fcc4/e+MY3bjDtxhtvvP7p7D777KNr\nr71WknTjjTfqiiuukCS9973v1cc//vGWl+OQQw7RpEmTtMcee+i5557T7NmzJUl77LGH7rnnHq1Y\nsUK333673vrWt0qSnnvuOW233XYtzw8AMM69TNvnSUbgCwBAHyh/ylv+Uz6Vv2W7ySabrP882py5\ncviECRO0bt26F81j0qRJ6/OulaZdo+XYaKONNpjfRhttpHXr1ikitPvuu+vGG2/s+LwBAOPQS5Xr\n7ilNnQEAaNH8A+d3Jd9tt91Wd9xxh55//nl9+9vf7so8ys2cOVPf/OY3JUkLFiyom3aLLbbQY489\n1vK8dtttN61Zs2Z94Pvss89q2bJlLecHAEAeBL4AALRotMlzp5122mk67LDDtN9++41JM+AzzzxT\nn//857Xnnntq5cqV2nLLLWum3XPPPTVhwgTttddeOuOMM5qe18Ybb6yFCxfqk5/8pPbaay/tvffe\n+tnPftZO8QEA40xlfxt5uLyZVNEMDQ3FaE+WAADUcscdd+g1r3lNr4vRM0888YQ222wz2daCBQt0\n6aWX6sorr+x1scb9dgEANObtrbg/GkbAvOMLAMA4t2TJEp100kmKCG211Va68MILe10kAAA6isAX\nAIBxbv/999ett966wbDbbrtNRx111AbDNtlkE910001jWTQAAOr7k3L9GDyBLwAAynpILu9Nebzb\nY489tHTp0p7Nv8ivYgEAOuhR3Z8nWa7OrWzPtr3C9krb86qM38T2ZWn8Tbanlo07OQ1fYfvgRnna\nnpbyWJny3LjRPAAAaMemm26qtWvXEmz1iYjQ2rVrtemmm/a6KACAgmj4xNf2BElnS3qrpFWSbrG9\nKCKWlyU7TtLDEbGr7bmSTpf0HtszJM2VtLuk7SX9wPar0jS18jxd0hkRscD2uSnvL9aaR7srAACA\nKVOmaNWqVVqzZk2vi4Jk00031ZQpU3pdDABAQeRp6ryvpJURcZck2V4gaY6k8sB3jqRS+rxQ0n85\nay82R9KCiHha0t22V6b8VC1P23dIerOk96Y0F6d8v1hrHsHteQBAmyZNmqRp06b1uhgAAKBL8jR1\n3kHSvWXfV6VhVdNExDpJj0jaus60tYZvLemPKY/KedWaBwAAAAAANeV6x3eQ2D7e9ojtEZqsAQAA\nAADyBL73Sdqx7PuUNKxqGtsTJW0paW2daWsNXytpq5RH5bxqzWMDEXFeRAxFxNDkyZNzLB4AAAAA\noMjyvON7i6TptqcpCz7n6oV3cEctknSMpBslHSHpuogI24skfd3255V1bjVd0s2SXC3PNM2PUh4L\nUp5X1ptHvYIvWbLkD7Z/m2MZUXzbSPpDrwuBQqFOodOoU+g06hQ6jTqFTutEndo5TyLn6RvK9qGS\nzpQ0QdKFEfEZ26dKGomIRbY3lXSJpNdJekjS3LKOq06R9EFJ6yT9XUR8t1aeafguyoLeV0j6paT3\nR8TT9eYBNGJ7JCKGel0OFAd1Cp1GnUKnUafQadQpdNpY1qlcgS8w6DhQo9OoU+g06hQ6jTqFTqNO\nodPGsk4VrnMrAAAAAADKEfhivDiv1wVA4VCn0GnUKXQadQqdRp1Cp41ZnaKpMwAAAACg0HjiCwAA\nAAAoNAJfAAAAAEChEfiicGxPsP1L21dVGXes7TW2l6a/D/WijBgctu+xfVuqLyNVxtv2WbZX2v6V\n7df3opwYHDnq1Czbj5Qdpz7Vi3JicNjeyvZC27+2fYftN1SM5ziFpuSoUxynkJvt3crqylLbj9r+\nu4o0XT9OTex0hkAf+JikOyS9rMb4yyLipDEsDwbfmyKi1o+rHyJpevr7S0lfTP+BeurVKUm6ISIO\nG7PSYND9p6TvRcQRtjeW9JKK8Ryn0KxGdUriOIWcImKFpL2l7AGVpPskfbsiWdePUzzxRaHYniLp\nryWd3+uyYNyYI+krkfm5pK1sb9frQgEYH2xvKekASRdIUkQ8ExF/rEjGcQq55axTQKsOknRnRPy2\nYnjXj1MEviiaMyV9QtLzddK8KzWhWGh7xzEqFwZXSPq+7SW2j68yfgdJ95Z9X5WGAbU0qlOS9Abb\nt9r+ru3dx7JwGDjTJK2R9OX0ms/5tl9akYbjFJqRp05JHKfQmrmSLq0yvOvHKQJfFIbtwyQ9GBFL\n6iT7H0lTI2JPSddKunhMCodB9saIeL2yJjgn2j6g1wXCwGtUp34haeeI2EvSFyRdMdYFxECZKOn1\nkr4YEa+T9CdJ83pbJAy4PHWK4xSalprNv0PSN3oxfwJfFMlfSXqH7XskLZD0ZttfLU8QEWsj4un0\n9XxJ+4xtETFoIuK+9P9BZe+j7FuR5D5J5S0HpqRhQFWN6lREPBoRj6fPV0uaZHubMS8oBsUqSasi\n4qb0faGyoKUcxyk0o2Gd4jiFFh0i6RcR8UCVcV0/ThH4ojAi4uSImBIRU5U1o7guIt5fnqbiXYF3\nKOsEC6jK9kttbzH6WdLbJN1ekWyRpKNTb4QzJT0SEavHuKgYEHnqlO1X2nb6vK+yc/XasS4rBkNE\n/F7SvbZ3S4MOkrS8IhnHKeSWp05xnEKLjlT1Zs7SGByn6NUZhWf7VEkjEbFI0kdtv0PSOkkPSTq2\nl2VD39tW0rfTuX2ipK9HxPdsnyBJEXGupKslHSpppaQnJH2gR2XFYMhTp46Q9Le210l6UtLciIhe\nFRgD4SOSvpaaEd4l6QMcp9CmRnWK4xSakm72vlXS35QNG9PjlKmjAAAAAIAio6kzAAAAAKDQCHwB\nAAAAAIVG4AsAAAAAKDQCXwAAAABAoRH4AgAAAAAKjZ8zAgCgT9neWtIP09dXSnpO0pr0/YmI2K8n\nBQMAYMDwc0YAAAwA2yVJj0fEf/S6LAAADBqaOqOjbJ9r+197XY5usX2p7cN7XY5W2N7f9oo646fa\nDttVW4LY/mfb53evhK2xfY/tJ21f0oW8r7P9lO2fNDFNyfZXO12WKvOpu70aTDvL9qo64y+y/en0\neU/bP2unrOgO24+n/7NsX2/7Stt32T7N9vts32z7Ntt/ntJNtv1N27ekv7/q7RKg2xodj2wvsz1r\nDIvUE6nu/9r2Zl3Ie7HtD9UYt5Ptx21P6PR8x4LtY20/l5bhNR3O+y0p3+dtv6WTefdCO+fkFuZV\ns87lmPaeWuu78tognUN2b7Wc/YjAt+DKgoLHbf8+XdBu3qG8j60MCCLihIj4t07kn2P+L7N9pu3f\npeW7M33fJo0fXfbHbP/R9s9sn2B7o7I8LrL9TJp+9O89Nea3p6S9JF2Zvm9ne5Ht+9PBbmqOMn/M\n9t22/2T7DtuvSsP3Shcgf7D9D2XpJ9m+yfaO7awrSYqIGyJit7K8ax78akz/7xHR6oH2TbZ/ZPsR\n2/fkSP8S2+ek9fGI7R83mOTtEXFUK2WrJyLeLOmEWuMbBZBFEBG/kvRH22/vdVlQ117K6uprJB0l\n6VURsa+k8yV9JKX5T0lnRMRfSHpXGocKzR4by6Zr+WK0VyJi94hYXC/NWF7Qd9E8SRdFxJOjA1Lg\n9Yt0Pl5l+92VE9k+Oi17S9s1In4XEZtHxHNtlL0h2+fZXpGCyGNzpG+47GX+//buO16Oqv7/+OuT\nnkBybxohDRK6iAoYQGyUANKjSAmigl++5mfhqwgqQRQCgoYqoCig0kIvAgFCbzZ6kR6IEEhICJDe\n2/38/jjn5k727m5u2d2Z3byfj8dNdmfOznxmZ3bOfGbOnHkiLsPrpYsY3P0hd98QeK+U062Utu4n\nqsx5wBlpB1FKSnzXDwfFncv2wA7AySnH025m1oVw39sngX2BXsCuwGxg50TRg9y9J7ApMB44Cfhr\nzuTOiTv1xr+bCsz2/wHXedP9AQ3AfYQDyJbE/L/AscABwIbAgcDHcfRvgZ8SDl5PMbON4/ATgNvc\nfVpL5pFhi4ErgJ+1sPzlQB/CQXwf4CdliitVVXQgeR1h+5fsesbdZ7r7cuC/wANx+MvAsPh6L+AP\nZvYiMBHoVaoToSJtVe79oJl1BY4Grk0M2xa4HjgFqCPUvc/lfK438Avg1XLGVyL/AX4APL+ugi1Z\n9mpURfUpUDXxTgT2SByTVj0lvusRd/8AuJ+QAAPNz1DnXsWNZzq/Z2Zvxauml1jwCeBSYNd4lXRe\nLJ9sIrl7PJP4czP70MxmmtlXzWx/M3vTzOaY2S8S8+pgZmPjldvZZnazmfUpsDjfBjYBvubur7l7\ng7t/6O6/dvdJeZZ9vrtPBI4Ajjaz7drwFe4HPJ6Y5ix3/yPwzLo+GK8ynwb8JMbr7v5fd58TiwwH\nHjd+ciUAACAASURBVHH394G3gE3MbFNCUv27dUz7ajM7Mb4eHNfZD+P7zeP33CF5ddJCs+BNgLvi\n+vt5YpJHWbiK/rGZnZKYz5omc4krAEfnK5vL3Z929wnA2y34rrYBDgbGuPtH7r7a3VtVKcfYfhC3\n24Vm9uv4XfzbzBbEbatLLNvPzO6O2/ccM/uHJVoFFJnHBsC9wCBrai0wKI7uYmbXxHm/amYjEp+b\namYnmdlLwGIz62Rmgyw0Qf3IQouAHyXK72xmz8a4Z5nZBTmhFFpfXS20gJgR/y6MB4D5lmUHC2f/\nF5rZTUC3nCKPASMLfV4yYXnidUPifQNNHVl2AD7n7tvHv8HuvqiSQVYzM+sd9xUfmdnc+HpIHHcW\n8CXCiYVFZvaHOHwbM3sw7lsmW+LKWqwvLzGze+Jv7ymLzdLj+E8mPjvLwu0mG5vZEgudnjWW2zHG\n1LlA6OvaH+0VXxfa1zS2uJkXl23XWKf80szetVC/X2NmdYnpfjuOm21mv8qZzzgzu9XMrjWzBcAx\ncd5PxP3wTDP7Q+M+On6mxfv0PHYB5rl7snXOL4HL3P1ed1/l7rPd/b85n/stcDFNJ6iL2dxCs9AF\nFm456BPjXutquYVjrl+b2b/icjxgTa3UusXvZHb8Hp4xswEtmDfufom7Pwwsa0Hxlix7QXH93RJj\nXWjhdoqtzOzkuC1MM7N9EuWPsXALxkIL9dtRrZjXcDP7e/zsQ/H3knsccqyZvQc8Eod/Lm4X88zs\nP5Zoym9mdWb217iNvW9mZ1pshh7j/KeZnWfh9/2Ome1XIK62HkPlbvcFj3tbsD1smm87ip892MJv\nfV7c5vI2Uzez7hb2Q3PN7DVgp+R4d19GOCnylXWtq2qhxHc9YqGC3g+Y0sqPHkj4MXwaOBz4Smzy\n8j2amsDUF/jsxoSD6MHAqcCfgW8CnyUcJPzKzIbHsv8HfBXYDRgEzAUuKTDdvYD7WnvQ5u5PA9Pj\nvFvMQpIzHCh4j+w6DIl/28VK4R0zO92aEqxXgH3iOhpGuGJzEfAzd1+5jmk/DuweX+9GSC6/nHj/\nD3dvSH4gNgt+j9gawN3PSYz+IrA1MBI4tdAOsw1lW2pn4F3g9FhxvGxmLbqqnuMrhO3sc8DPCVeR\nvwkMBbYDjozlTiRsE/2BAYQz/Ovs9c/dFxN+TzMSrQVmxNEHAzcC9YQzpn/I+fiRhCv/9YTE5C7C\nGfvBhO/yeDNrrGguAi5y917A5sDNOdMqtA5Oicu+PeGM/s6EA561xIPFO4AJhKvrt5DTiiGekFkZ\n5yPV6wGamj1jZtsXKSvNdQCuJLQg2gRYSvxtu/spwD+A4+K+4LhYbzxIuLq2ETAa+KOFK26NRgOn\nA70JdfNZAGbWE3iI0KpoELAF8HA8gf0YoS5u9C3gxiJ1xbr2R40K7Wsa65P6uGxPAMfEvz2AzQit\nmBqT/W2BPwJHAQMJVxUH58xrFHBrjOk6Qm/lPwH6EVpvjSRcwUxq6T4916doXnd/Lsb6ckyCrrXE\niXYz2xkYQTjB3xLfBv6HsLyrCAlzId8AvkPYJroQWntBuCpdF5enL+EYa2m+CbRT0WVvoYMIdUZv\n4AXCRZUOhPV8BnBZnMcGhO9iv9j67vPAi62Yz/XA04TvYxxhW8+1G6F12FfMbDBwD3AmoT77KXCb\nmfWPZa8irJ8tCC0g9wGSzdh3IWwr/YBzgL+ameXOsB3HULnbfbHj3nVtD3m3Iwu30N0AHE84rplE\nSNDznRg6jfBb35zw+zo6T5nXCccQNUGJ7/rhDjNbCEwDPiRs6K0x3t3nuft7wKMkrhi3wErgrFgh\n30jYmVzk7gvd/VXgNZp+UN8DTnH36bG53jjgUMvfHKQvMLOVy9FoBmGH2Oin8azYPDMrdGa3MbFf\n2MZ5Don/70OohPcgVNLHNsYAfJ9wUPIT4AtxXu9YOHv8uJkdVmDajwNfjEn0lwk768ZOa3YjcZW6\nhU5396Xu/h9CMlZsh9easi01hHAQM59QERwHXN2GpPocd18Qt7NXgAfc/W13n0+4UrtDLLeScLCy\nqbuvjPdCt7e7+3+6+6R4X9cEmn8vF7v7tHi/2U5Af3c/w91XuPvbhBNEoxPxbWFm/dx9kbs/mTOt\nQuvgKOCM2BLiI8LBdb6Dhs8BnYEL4/LfSv5WDAtp+h1IdfoRMMLMXopn9wvevy7NxStjt7n7Endf\nSEhSdyvykQOBqe5+Zbyy9gJwG5Dcl98eW8SsIhwIb5/47Afufr67L4t15lNx3NWEhI94tepIwn6m\nkHXtjxqta1+TdBRwQdynLiLcQjU61teHAne5+z/dfQXhpHfuPvUJd78jttZa6u7PufuT8XuaSkic\ncr/blu7Tc9XTvO4eQtgffh3YEugO/B7WfKd/JJzEaKBlJrj7K/GE6K+Aw61wh1ZXuvubcf9/M03r\nfCXh2GaLxpZO7r6ghfNvjYLL3gr/cPf743Z7CyHBGp841htmZo31RQPhpH93D7djtKjpuJltQqgf\nT4114z8Jx0i5xrn74vh9fhOYFLf3Bnd/EHgW2D9eLd0fOD6W/5DQom50Ylrvuvuf42/lasKxQYuu\nuicUOy5aa7un+HHvuraHQtvREcA97v5gXB/nEdZxvkffHU44Rp/j4Za6fCdsaqruV+K7fvhqPNO2\nO7ANIflsjQ8Sr5cQzuy21Gxv6tSh8UzVrMT4pYnpbQrc3piEEs4yrSb/Tmc2YYfUFoOBOYn357l7\nffwr9N3Mi//3bOM8G5f9nHgSYSqhYt8fwN3fdff93X1HQudZvyYkw+cBNxHO2F+Q76yshyZKiwk7\nvS8BdwMzzGxr2pb4tmZ9t2fbKGQpYYd/ZqzsHieccNmn+Meayd3OCm135xKutDxgoTnW2LaFvZbc\n76Vbzgmc5D3bmxKaS89LbPu/oGm7PxbYCngjNnU6cB3zalyuQYQr543ejcNyDQLez0n2381TridN\nvwNJgbuP88SjjDz03YC7P+buByaG7+7uz+aOc/eP3f0Id/+0u2/r7kp8W8FCp3uXWWjCu4DQBLi+\nSIKzKbBLzm/7KEJLqEaFfr9DCS1/8rkT2Da2ltobmO+hNVMh69ofNVrXviYp3/6lE2G/NYjEPs7d\nlxDq7KS1+q2w0FT2bgudcC4AfkPzY5WW7tNzzaV53b2UpsRhUZzf/nHcD4CX1pH450ouz7uEk4mF\njicKrfMJhCunN1q4PeUcK9x8vT2KLXtL5X73H+c51tswngg4gpDgzbTQrH+bFs5jEDAnbj+N8vV3\nklufHpbzm/si8eQ2Yb3MTIy7jHDFtNGadZOYb2uPa4odF+XGX+y4d13bQ4vq/njyZhrNW100ls3d\ndnPVVN2vxHc9EhOIqwjJVKPFQI/E+9bcwF7qh0BPIzSHqU/8dfPQzDLXQ4RmLRu0ZgZmthPhx9/i\nx9PAmmat/yUcFLTFZGAFa39nhb6/U4E/u/sswtXhZ+MZ7emE5jn5PE44y94lfl+PE5qs9KZws6Ks\nPsT7pTzDyhZrvJJyortvRjjBcIKZjWzpx9s628TracA7Odt9T3dvPCnylrsfSaiczwZubeF2P4NQ\nqTbaJA7LNRMYnNOca5Nkgdh8rAttb+ovUgtOJDRh3MVDc+DGJsCNv53c/cE04PGc3/aG7v79Fsxr\nGqEJcTMe7ru7mXB161sUv9rbYkX2Nfn2c/n2L6sICdFMmlo5YeERQn1ZW+40/wS8AWwZv9tf0PS9\nttdLNK+7X6JwfTwS+FpMwj8gXCk73+J92wUkn7ywCeHkbUvuDW4KILS4Od3dt43zPJDQhLrUii17\nycUrw3sTks83CC2aWmIm0MfMkseo+Z5wkVufTsj5zW3g7uPjuOVAv8S4Xu7e1sf1tOV7y7ePyHvc\n247tYa3fZqzbhwL5jqVn0nzbzfUJwpXrmqDEd/1zIbC3mTU2vXgROCSeyd6Cpqa3LTELGFLgvoG2\nuBQ4y0KnTlh47t6oAmUnEHYYt1noPKSDmfW10PlHszOXFh59dCChCc617v5yG+KbRE7TKzPrBjR2\n+NM1vm8mnjm8Cfi5mfW0cC/vGMLV2eT0tiVcmf9THPQOsGdsorMlhbv9f5zQJLixE5LH4vt/euHH\nKMyiwIFVqcX1041wttUsdNpQaLv5O2E5T7bQ8dMXCE3D7y9TbAea2RaxcphPONva0uZts4C+lujU\npQ2eBhZa6PCqu5l1NLPt4kkazOybZtY/nrVtPOvakvhuAH4Zf0f9CCdU8j3P8wnCAeuPLDw+6xDW\n7hkdwnb/SGyKJbI+6Bz3U41/nQhXPpYSOnnqQ/PbhnL3qXcDW5nZt+Jvq7OZ7dTC2zbuBgaa2fEW\nOqrraWa7JMZfQ7jH9mBKlPgW2dd8FP9PLtsNwE8sdD60IeGq4U0emr7eChxkZp+P+/lxrDuJ7Qks\nABbFK4ItOTnQUk8Trswnr3hdCXzHzDaLidVYmurjYwgH+9vHv2cJt4oU7MAR+KaZbRundQZwa5G6\nNy8Lj/37VGxBsICQPDfEcePM7LEin+0S61ijadstdIxfbNlLyswGmNmoeAJlObCIFtav7v4u4bsf\nF5dvV8K9xcVcS9j2vhLr0m4WOvYc4u4zCf0cnB+PCTtY6CCt2O0KxZTiGKrgcW+x7WEdbgYOMLOR\n8QrxiYTv/t8Fyp5soeO+IST6gIgxdCPcV/9g2xYve5T4rmc83Ot3DeEgGML9DSsIP+CrCfcYtdQj\nhG7+P7DC98a2xkWE+zcesHBP8pOEjgaaiQfgexHOHj5I2Ck8TWha9FSi6F3WdH/zKcAFhM4A2uJy\nQm99yQp8KWFHTowl+YzAS80s2THGcbHsDEKycT3hET9JlwA/TlSYJxPuy3sV+I2Hjk3yeZxw4NCY\n+P6TcCW/2PNvf0tIjOaZ2U+LlCuFLxO+m0k0dQrT+LgVLPQ+eBSEs96EDiD2JySifwa+7e5vlCm2\nLQktCBYR1ssf3f3RlnwwxnQD8Hb8HvM1JV7XNFYTzuRuTzjR8THh+aqNyfS+wKtmtojwGxntiWdR\nFnEm4aDhJcIjbZ6Pw3LnvwI4hHCwN4fQLO1vOcWOouWdvIjUgkmE/VTj3zjCiePuhN/ok4SOp5Iu\nItyfN9fMLvZwH/A+hHsIZxCaJp5N08nSguJn9yYc6H9A6O1/j8T4fxEOgp+PCUIp5N3XxBO3ZwH/\nivu5zxHqrgmEOuYdQm/C/xdjezW+vpFwRWkRoX+RYifOfkrorGchYZ9f6LGCrRb3cVcR74uOw64g\nHAs9RWjeuZxQ1xJvR/qg8Y9wjLQgtrwqZEKcxweEDj1/VKRsIRsTThosIDR5fZymkxpDgX8V+ewD\nhO3084RjlaXEFglmdpSZrbmvttiyl0EHwmMZZxDql91o3UmNo2h6VOWZhO2i4Hbk4T7VUYQWAx8R\njv1+RlO+821C66XXCE3gb6Xtt82V4hiq2HFvse2hIHefTNjWf0/YVx1E6IRrRZ7ipxO2gXcI21Du\n9A8CHvOmjjurnnm7+3ARWX+Y2fXAze5+R9qxSGBmkwkV1+3unq9HwvZM+0FC509Pu3tLmz/XFDP7\nNOHRF7umHYuINDGzR4Dr3f0vacdSTLwiPI/QjPmdlGLoT+h1e4cWnjjMFAvP3h7p7rn3Spd7vt8i\n3Ae7AtjVwxM9SjXtkYTO3roC+7fkhLOFx+294e6t7aRV2sDMngKOdfdX0o6lVJT4ioiIiFSReCvE\ng8DQeHU4U8zsIOBhQtPb8wlXsXZ0HXRKK8TtfA7hiuQ+hEfv7eqhh3SRVlNTZxEREZEqYWZXE27P\nOD6LSW80itC8dQbhdpLRSnqlDTYm9FmyiPCone8r6ZX20BVfERERERERqWm64isiIiIiIiI1TYmv\niIiIiIiI1LROaQdQTj3qenj9xvVphyEikgmDerb6aUuS47nnnvvY3funHUc1U90sIlmlerI6tbRu\nrunEt37jesZcNibtMEREMmHc7uPSDqHqmVmpnpm63lLdLCJZpXqyOrW0bs5MU2czu8LMPjSzvM+K\nsuBiM5tiZi+Z2Y6VjlFERERERESqT2YSX+AqYN8i4/cjdIm/JTAG+FMFYhIREREREZEql5nE193/\nTnhIdSGjgGs8eBKoN7OBlYlOREREREREqlVmEt8WGAxMS7yfHoeJiIiU1fJVqzn7vjfSDkNERETa\nqOY6tzKzMYSm0NQNqEs5GhERyaJ3Zy9m4oszWLRiFYuXr2LJ8tUsWr6KxStWsXj56jBsRRi2ZMUq\nVq72tEMWEZH1xLCx9+QdPnX8ARWOpLZUU+L7PjA08X5IHLYWd78cuBxg0NaDdKQiIiJrWd3gHH3F\n00ydvYSunTqwQddObNC1Ixt06cQGXTvRq3tnBtZ1C8O7dIzjw+vvnJ129CIiItIW1ZT4TgSOM7Mb\ngV2A+e4+M+WYRESkyrw+cwFTZy/hvMM+w6GfHdKqz36nTDGJiIhIeWUm8TWzG4DdgX5mNh04DegM\n4O6XApOA/YEpwBJ0/CEiIm2wZMVqAPr37JpyJCIiIlIpmUl83f3IdYx34IcVCkdERGrUv6Z8DMBW\nAzZMORIRERGplMwkviIiIuW0usG55omp/OHRKYzcZiMG1nVPOyQRERGpECW+IiJS8z5cuIxv/uUp\n3py1iJHbbMRFR+6QdkgiIiJSQUp8RUSkprk7tz43nTdnLeLHI7fkxyO3pEMHSzssERERqSAlviIi\nUrMmf7CQ4296kddnLuAzQ+o4bs8tlPSKiIish5T4iohIzfrh9c8zb8lKzj3004zafjCdO3ZIOyQR\nERFJgY4ARESkJs1fspIZ85ay33Ybc9iIoXTppCpPRERkfaWjABERqUkXPvwmK1Y1cNBnBqUdioiI\niKRMia+IiNSkh16fxe5b92fn4X3SDkVERERSpsRXRERqztIVq/lg/jK22Khn2qHUDDP7iZm9amav\nmNkNZtbNzIab2VNmNsXMbjKzLmnHKSIiko8SXxERqTkfLVzOytXO5v03SDuUmmBmg4EfASPcfTug\nIzAaOBv4nbtvAcwFjk0vShERkcKU+IqISM2ZOX8pAL26d045kprSCehuZp2AHsBMYE/g1jj+auCr\nKcUmIiJSlBJfERGpOQ+8NosuHTvw+c37ph1KTXD394HzgPcICe984DlgnruvisWmA4PTiVBERKQ4\nJb4iIlJTXp4+nwlPvssnB/eiZzdd8S0FM+sNjAKGA4OADYB9W/H5MWb2rJk9u2T+kjJFKSIiUpgS\nXxERqRnvfLyYQy/9Nz27duLYLw5PO5xashfwjrt/5O4rgb8BXwDqY9NngCHA+/k+7O6Xu/sIdx/R\no65HZSIWERFJUOIrIiI149z736BLpw5M+vGXOPDTen5vCb0HfM7MepiZASOB14BHgUNjmaOBO1OK\nT0REpKjMJL5mtq+ZTY6PRBibZ/wmZvaomb1gZi+Z2f5pxCkiItk1c/4yth9az4Be3dIOpaa4+1OE\nTqyeB14mHD9cDpwEnGBmU4C+wF9TC1JERKSITusuUn5m1hG4BNib0DnGM2Y20d1fSxT7JXCzu//J\nzLYFJgHDKh6siIhk0pzFK5jy4SL2227jtEOpSe5+GnBazuC3gZ1TCEdERKRVMpH4EirNKe7+NoCZ\n3UjoRCOZ+DrQK76uA2ZUNEIREcmEhgZnykeLePujxbw3ZzHvzl7Ce3OW8MzUOSxf1cAhOw5JO0QR\nERHJmKwkvoOBaYn304FdcsqMAx4ws/8j9Ca5V2VCExGRLFi6YjVn3/cG973yAR8sWLZmeF33zmza\ntweH7DiEYz4/jK0G9EwxShEREcmirCS+LXEkcJW7n29muwITzGw7d29IFjKzMcAYgLoBdSmEKSIi\n5XDTM+9x1b+nsuc2G3HiPlux9cY92bTPBtT10COLREREpLisJL7vA0MT7/M9EuFY4jMD3f0JM+sG\n9AM+TBZy98sJHW4waOtBXq6ARUSksmYvXoEZXHHMTmmHIiIiIlUmK706PwNsaWbDzawLMBqYmFPm\nPcLjEzCzTwDdgI8qGqWIiKRm+aoGOpilHYaIiIhUoUwkvu6+CjgOuB94ndB786tmdoaZHRyLnQh8\n18z+A9wAHOPuuqIrIrIeeO7dOVzzxFS2H1qfdigiIiJShbLS1Bl3n0R4RFFy2KmJ168BX6h0XCIi\nkr67X5rJspUN/OmoHdMORURERKpQJq74ioiIFPPEf2ezy/A+bNSrW9qhiIiISBVS4isiIpm2cnUD\n73y8mO0Gq6d+ERERaRslviIikmkz5y1j+aoGthqwYdqhiIiISJVS4isiIpnmhH4MO3dUlSUiIiJt\no6MIERERERERqWlKfEVERERERKSmKfEVERERERGRmqbEV0REZD1jZp9KOwYREZFKUuIrIiKy/vmj\nmT1tZj8wMz0nSkREap4SXxERybTVDZ52CDXH3b8EHAUMBZ4zs+vNbO+UwxIRESkbJb4iIpJpb85a\nBMCmfXukHEltcfe3gF8CJwG7AReb2Rtmdki6kYmIiJSeEl8REcm0lasbAKjr3jnlSGqHmX3azH4H\nvA7sCRzk7p+Ir39X4DP1ZnZrTI5fN7NdzayPmT1oZm/F/3tXcDFERERaTImviIhkmho6l8XvgeeB\nz7j7D939eQB3n0G4CpzPRcB97r4N8BlC0jwWeNjdtwQeju9FREQyp1PaAYiIiBQz9ePFmEG/Dbum\nHUotOQBY6u6rAcysA9DN3Ze4+4TcwrEDrC8DxwC4+wpghZmNAnaPxa4GHiM0nRYREckUXfEVEZFM\nW7R8Fd06daS+R5e0Q6klDwHdE+97xGGFDAc+Aq40sxfM7C9mtgEwwN1nxjIfAAPKEq2IiEg7ZSbx\nNbN9zWyymU0xs7xNpczscDN7zcxeNbPrKx2jiIhIjejm7osa38TXxXoP6wTsCPzJ3XcAFpPTrNnd\nnQIt081sjJk9a2bPLpm/pN3Bi4iItFYmEl8z6whcAuwHbAscaWbb5pTZEjgZ+IK7fxI4vuKBioiI\n1IbFZrZj4xsz+yywtEj56cB0d38qvr+VkAjPMrOBcRoDgQ/zfdjdL3f3Ee4+okedeucWEZHKy8o9\nvjsDU9z9bQAzuxEYBbyWKPNd4BJ3nwvg7nkrVxERqS0Ll62kU0dLO4xaczxwi5nNAAzYGDiiUGF3\n/8DMppnZ1u4+GRhJqKNfA44Gxsf/7yx75CIiIm2QlcR3MDAt8X46sEtOma0AzOxfQEdgnLvfV5nw\nREQkLe98vJitBvRMO4ya4u7PmNk2wNZx0GR3X7mOj/0fcJ2ZdQHeBr5DaDl2s5kdC7wLHF6umEVE\nRNojK4lvS3QCtiT0HjkE+LuZfcrd5yULmdkYYAxA3YC6SscoIiIlNm/JSvr3VI/OZbATMIx4/66Z\n4e7XFCrs7i8CI/KMGlme8EREREonK4nv+8DQxPshcVjSdOCpeEb6HTN7k5AIP5Ms5O6XA5cDDNp6\nkB7/KCJS5abNWcLnN++Xdhg1xcwmAJsDLwKr42AHCia+IiIi1Swrie8zwJZmNpyQ8I4GvpFT5g7g\nSMKjFPoRmj6/XdEoRUSkohYuW8nSlavZsFtWqquaMQLYNvbELCIiUvMycSTh7qvM7DjgfsL9u1e4\n+6tmdgbwrLtPjOP2MbPXCGenf+bus9OLWkREyu359+bR4LC17vEttVcIHVrNXFdBERHJhmFj78k7\nfOr4AyocSXXKROIL4O6TgEk5w05NvHbghPgnIiLrgccmf0inDsZuW/dPO5Ra0w94zcyeBpY3DnT3\ng9MLSUREpHwyk/iKiIjkev7duXxycB0bdlV1VWLj0g5ARESkkjqkHYCIiEghb3ywkJ027Z12GDXH\n3R8HpgKd4+tngOdTDUpERKSMlPiKiEgmLVq+ihWrG+ihq70lZ2bfBW4FLouDBhM6kRQREalJSnxF\nRCSTZsxbijtssdGGaYdSi34IfAFYAODubwEbpRqRiIhIGSnxFRGRTPp4Yehzqaeu+JbDcndf0fjG\nzDoRnuMrIiJSk5T4iohI5vzjrY/4wfXhltOtN9ajjMrgcTP7BdDdzPYGbgHuSjkmERGRslHiKyIi\nmfLGBwsYc81zbNClE+d8/dMMrOuWdki1aCzwEfAy8P8IjxP8ZaoRiYiIlJHaj4mISKaceuerdO5o\n3Pr9XRlY1z3tcGqSuzcAf45/IiIiNU+Jr4iIZIa78/rMBey33cZKesvIzN4hzz297r5ZCuGIiIiU\nnRJfERHJjJWrnYXLVjGkd4+0Q6l1IxKvuwGHAX1SikVERKTsdI+viIhkxkOvzwJgs/4bpBxJbXP3\n2Ym/9939QuCAtOMSEREpF13xFRGRzPgoPsJo1836phxJbTOzHRNvOxCuAOuYQEREapYqORERyRwz\nSzuEWnd+4vUqYCpweDqhiIiIlJ8SXxERyYzVDc36W5IycPc90o5BRESkkpT4iohIZkz5aBE9u3Wi\nrnvntEOpaWZ2QrHx7n5BpWIRERGphMx0bmVm+5rZZDObYmZji5T7upm5mY0oVEZERKrT8pUN9OrW\nmY4d1NS5zEYA3wcGx7/vATsCPeNfXmbW0cxeMLO74/vhZvZUrLtvMrMuFYhdRESk1TKR+JpZR+AS\nYD9gW+BIM9s2T7mewI+BpyoboYiIlNvi5at4eupsenZTY6QKGALs6O4nuvuJwGeBTdz9dHc/vcjn\nfgy8nnh/NvA7d98CmAscW7aIRURE2iETiS+wMzDF3d929xXAjcCoPOV+Tahkl1UyOBERKb/XZi5g\n2pylfH/3zdMOZX0wAFiReL8iDivIzIYQHnn0l/jegD2BW2ORq4GvljxSERGREshK4jsYmJZ4Pz0O\nWyM+emGou99TycBERKQyPPZr1XeDrukGsn64BnjazMaZ2ThCS6qr1/GZC4GfAw3xfV9gnruviu+b\n1d2NzGyMmT1rZs8umb+k3cGLiIi0VlYS36LMrANwAXBiC8qqchURqUKvzpgPwODe3VOOpPa5CEpY\naQAAE/RJREFU+1nAdwjNk+cC33H33xQqb2YHAh+6+3NtnN/l7j7C3Uf0qOvRpphFRETaIyuJ7/vA\n0MT7IXFYo57AdsBjZjYV+BwwMV8HV6pcRUSq04vT5rFxr24M77dB2qGsL3oAC9z9ImC6mQ0vUvYL\nwMGxDr6R0MT5IqDezBpvys6tu0VERDIjK4nvM8CWsXfILsBoYGLjSHef7+793H2Yuw8DngQOdvdn\n0wlXRERK7c1Zi9h8IyW9lWBmpwEnASfHQZ2BawuVd/eT3X1IrINHA4+4+1HAo8ChsdjRwJ1lC1pE\nRKQdMpH4xvuDjgPuJ/QWebO7v2pmZ5jZwelGJyIi5bZqdQPvzl6sq72V8zXgYGAxgLvPoMhjjIo4\nCTjBzKYQ7vn9a8kiFBERKaHMPDPC3ScBk3KGnVqg7O6ViElERCpj+aoGlqxYzdDeukWlQla4u5uZ\nA5hZi884uPtjwGPx9duEJzOIiIhkWiau+IqIiACYpR3BeuNmM7uMcI/ud4GHgD+nHJOIiEjZZOaK\nr4iIiFSGu59nZnsDC4CtgVPd/cGUwxIRESkbJb4iIiLrETPrCDzk7nsASnZFRGS9oKbOIiKSulkL\nlgGwYdfOKUdS+9x9NdBgZnVpxyIiIlIpuuIrIiKpm7VgOQDD+qlzqwpZBLxsZg8Se3YGcPcfpReS\niIhI+SjxFRGRzDDUu1WF/C3+iYiIrBeU+IqIiKwnzGwTd3/P3a9OOxYREZFK0j2+IiIi6487Gl+Y\n2W1pBiIiIlJJSnxFRETWH8m25JulFoWIiEiFKfEVERFZf3iB1yIiIjVN9/iKiIisPz5jZgsIV367\nx9fE9+7uvdILTUREpHyU+IqIiKwn3L1j2jGIiIikQU2dRUREREREpKYp8RURkdTNX7oSgC6d9Bxf\nERERKT0lviIikrr35iwGYIuNeqYciYiIiNSizCS+ZravmU02sylmNjbP+BPM7DUze8nMHjazTdOI\nU0REyqdTB13xFRERkdLLROJrZh2BS4D9gG2BI81s25xiLwAj3P3TwK3AOZWNUkRERERERKpRJhJf\nYGdgiru/7e4rgBuBUckC7v6ouy+Jb58EhlQ4RhEREREREalCWUl8BwPTEu+nx2GFHAvcW9aIRERE\nBAAzG2pmj8Zbjl41sx/H4X3M7EEzeyv+3zvtWEVERPKpuuf4mtk3gRHAbgXGjwHGANQNqKtgZCIi\nIjVrFXCiuz9vZj2B58zsQeAY4GF3Hx/75xgLnJRinCIiAgwbe0/e4VPHH1DhSLIjK1d83weGJt4P\nicPWYmZ7AacAB7v78nwTcvfL3X2Eu4/oUdejLMGKiIisT9x9prs/H18vBF4ntMwaBVwdi10NfDWd\nCEVERIrLSuL7DLClmQ03sy7AaGBisoCZ7QBcRkh6P0whRhERkfWemQ0DdgCeAga4+8w46gNgQEph\niYiIFJWJxNfdVwHHAfcTziLf7O6vmtkZZnZwLHYusCFwi5m9aGYTC0xOREREysDMNgRuA4539wXJ\nce7ugBf43Bgze9bMnl0yf0m+IiIiImWVmXt83X0SMCln2KmJ13tVPCgREREBwMw6E5Le69z9b3Hw\nLDMb6O4zzWwgkLdFlrtfDlwOMGjrQXmTYxERkXLKxBVfERERyS4zM+CvwOvufkFi1ETg6Pj6aODO\nSscmIiLSEpm54isiIiKZ9QXgW8DLZvZiHPYLYDxws5kdC7wLHJ5SfCIiIkUp8RURkdTNmLeMrp06\n0KWTGiJlkbv/E7ACo0dWMhYREZG20BGGiIik7uNFyxlc353OHVUtiYiISOnpCENERLKh0PVEERER\nkXZS4isiIiIiIiI1TYmviIiIiIiI1DQlviIiIiIiIlLT1KuziIiIiIjIemDY2HuaDZs6/oAUIqk8\nXfEVEZHUNbinHYKIiIjUMCW+IiKSKnfnhffmsXn/DdMORURERGqUEl8REUnVgmWrmDl/GTsN6512\nKCIiIlKjlPiKiEi6Yivnjh1UJYmIiEh56ChDRERS9c7sxQBs1LNrypGIiIhIrVLiKyIiqVq4bCUA\nG9d1SzkSERERqVWZeZyRme0LXAR0BP7i7uNzxncFrgE+C8wGjnD3qZWOU0SkmrjHP4ylK1az2p3V\nDU5Dg7Pam/4Pw2h6Hf9f89dYds1r8gzznM+TZ1jOeHdenDYPgCG9u6f8bYmIiEitykTia2YdgUuA\nvYHpwDNmNtHdX0sUOxaY6+5bmNlo4GzgiMpHKyJSfu4wf3E35izswfxF3Zi/uBsLFndj5aqONLjh\nWExqk/8bDmF8HAa2ZpoTHrgvteVZly9v1Z+BdUp8RUREpDwykfgCOwNT3P1tADO7ERgFJBPfUcC4\n+PpW4A9mZu6FH/7Yc/ZCxu1xerPh599yAgv79WT3qx5j96sfz9z4c28O4/e46jH2uKb5+HNuOoEF\nfXux59WPsueE5uPH33AiC/v1Ys9rHmXkhMeajf/NDT9lYd9ejLzmEfa6tvn4s67/2Zrxe1/3aLPx\nZ177cxb07cVe1z7MPnnGnzHhJBb07cU+1z7MPtc/0mz86deMZX7fXnzl2of4yg3Nx5921cnM79uL\nfa9/iH1veLjZ+F9d+QsW9O3Fvtc/yH43Nh9/yhW/YEHfOva74UH2v/GhPONPYX6fOva74QEOuKn5\n+JP/cgrz+9RzwI0PcMDNDzYbf9Kff8n8PnUceNMDHJhn/M8v/xXz+tRx0E33c9Atzcf/9NJTmd+n\njoNuvp+Db32g2fgT/3Qa8/rUcfAt9zEqz/if/HEc8/rUMeqW+/jqbfc3G3/8JafH8ffytb81H/+j\nP5zBvN51fO22e/na35onQsf9/tfM613HIbdN4pDbm4//4cVnMrd3HV//2yS+fvu9zcb/4MIzmdu7\nnq/ffg+H3tF8/Pd+dxZze9dz6O33cNidk5qNH3PBb5jbu57D7ribw/OM/+75v2Vu73oOv+NuDp/Y\n/CHsx543nrn19Rxx510ccVfz8f9z7njm1Pdm9MS7GH3X3c3Gf+ecs5ld35sjJ07kyLubjz96/LnM\nqa/nyLsm8o177mo2/lu/PZc59b35xt13clSe8Uf95jzm1Ndz1N0T+eakic3Gf+Os8/ioV1++dtsk\nTn/slmbjDzv9Qub0rufoe2/n2/fdmWf+5zG3dx3fuPtOvpEn/mtv+xfL+m/MZ6+8iB2uvLjZ+Hvv\nf4YVGw3kE5ddwFaXXtBs/NP/eJlVAzdmk4vPYcjF5zYbP+WFyTBoIH3P/S29z/tts/Fz33qHDoMG\n0+03Z9D1rDPXHnks8P77MGgQjBsHpzfff6c+XkRERKqSFckbKxeE2aHAvu7+v/H9t4Bd3P24RJlX\nYpnp8f1/Y5mPC0130NaDfMxlY9oU0w0Pb09DQ7gFOt831PS12dplEoWbXtqaAc2GiUgGhV+qrfkn\nZ1gjI2dYLJPn571mkOXZC6w1nTC+X91ihm40j416L6JXj2V06tj+ffW43ce1exrrOzN7zt1HpB1H\nNWtP3SwiUk5ZqSeHjW1+4r6YqeMPaNc08n2+mrS0bs7KFd+SMbMxwBiAugF1bZ7OFoNnkzwn0PxA\n1vMe3ELjwXKeg+Q4wPKm0mvPY02ZtYblfqD5PPLF2RRTnjhjmYLLYs1jTR6sN1uWZolAIoZ8869o\nUpFnWfKt14LzJ+933nxaOWXyrsOm9Zs7rdzp5J9W8/XaPKb2bKPNY1g7tnV85znj2r2Nmuf9zpum\n14I4c35TxWMSERERkVqSlcT3fWBo4v2QOCxfmelm1gmoI3RytRZ3vxy4HMJZ5bYGtNM209r6URER\nEREREcmQrDzO6BlgSzMbbmZdgNFA7s1vE4Gj4+tDgUeK3d8rIiIilWFm+5rZZDObYmZj045HREQk\nVyau+Lr7KjM7Drif8DijK9z9VTM7A3jW3ScCfwUmmNkUYA4hORYREZEUtfDJDCIiJXXhQ2/mHX78\nXltVOJL0tfae4PVVJhJfAHefBEzKGXZq4vUy4LBKxyUiIiJFteTJDO2mg1wRaYl8+wrtJ9omX0Jd\nzR1hZaWps4iIiFSnwUCyY4zpcZiIiEhmZOJxRuViZguByWnHUSL9gIKPbqoyWpZsqpVlqZXlAC1L\nFm3q7v3TDiJLWvhIwjVPXAC2A16peKDtV83bcLXGrrgrS3FXXrXGnrW4W1Q3Z6apc5lMrpXnLZrZ\ns1qW7NGyZE+tLAdoWaRqrPPJDMknLlTrtlCtcUP1xq64K0txV161xl6tcaups4iIiLRHS57MICIi\nkqpav+IrIiIiZVToyQwphyUiIrKWWk98L087gBLSsmSTliV7amU5QMsiVSLfkxmKqNZtoVrjhuqN\nXXFXluKuvGqNvSrjrunOrURERERERER0j6+IiIiIiIjUtEwmvma2r5lNNrMpZjY2z/iuZnZTHP+U\nmQ1LjDs5Dp9sZl9Z1zRjZxxPxeE3xY45is4jo8txXRz+ipldYWad4/DdzWy+mb0Y/05t7XKksCxX\nmdk7iZi3j8PNzC6O5V8ysx2rYFn+kViOGWZ2Rxye5fVyhZl9aGav5Eyrj5k9aGZvxf97x+HtXi8V\nXo5zzeyNGOvtZlYfhw8zs6WJdXJpa5cjhWUZZ2bvJ2Lef13TyvCy3JRYjqlm9mIcXpL1Iu1Xpu2h\n7HVzheMuWV1c4bhLVu9WOO6S1bFlirvs9WmF4y5p/Vnh2EtWX1Y47pLVjaWO28yGmtmjZvaamb1q\nZj9OlC/bMWO7uHum/ggdY/wX2AzoAvwH2DanzA+AS+Pr0cBN8fW2sXxXYHicTsdi0wRuBkbH15cC\n3y82jwwvx/6Axb8bEsuxO3B3la2Tq4BD88SxP3BvXMbPAU9lfVlypnsb8O0sr5c47svAjsArOdM6\nBxgbX48Fzi7FeklhOfYBOsXXZyeWY1hu2SpYJ+OAn+aJo+C0srosOdM9Hzi1VOtFf+3/K8f2UGya\nlKhuTiHuktTFKcR9FSWodysdd85021zHliPuOK6s9WkKcZes/kwh9nGUoL6sdNw5021z3ViOuIGB\nwI6xTE/gTZr2KWU5ZmzvXxav+O4MTHH3t919BXAjMCqnzCjg6vj6VmCkmVkcfqO7L3f3d4ApcXp5\npxk/s2ecBnGaX13HPDK3HBA6FvEIeJrwHMVSqeiyFDEKuCYu5pNAvZkNrIZlMbNehG3tjlbGW+ll\nwd3/DszJM7/ktHJ/K+1ZLxVdDnd/wN1XxbdPkv3fSrF1UkjBaWV9WeLnDyckDZId1Vo3V2tdXK31\nbrXWsdVan1Zz/Vmt9WW11o0lj9vdZ7r78zH+hcDrwOA80yrlMWO7ZDHxHQxMS7yfTtOX2KxM/AHO\nB/oW+Wyh4X2BeYkfcXJeheaRxeVYw0Kzqm8B9yUG72pm/zGze83sk61YhjSX5azYBOJ3Zta1FXFk\ncVkg/OAfdvcFiWFZXC/FDHD3mfH1B8CAVsRRTKWXI+l/CGceGw03sxfM7HEz+1IrptMsziLxlHpZ\njou/lSsamxK1Y1p54ywyjXKsly8Bs9z9rcSw9q4Xab9qrZurtS6u1nq3WuvYaq1Pq7n+rNb6slrr\nxrLGHZtF7wA8FQeV65ixXbKY+Er7/BH4u7v/I75/HtjU3T8D/J7SXnEsl5OBbYCdgD7ASemGUxJH\nsvZZumpcL2vEqxmedhztYWanAKuA6+KgmcAm7r4DcAJwfbyKkGV/AjYHtifEf3664ZRE7m+lGteL\nSLXVxdVe71ZtHVuN9WmV1p/VXl9mtm40sw0Jtxocn3PyCcjWNp7FxPd9YGji/ZA4LG8ZM+sE1AGz\ni3y20PDZhEvsnXKGF5tHFpeDOI3TgP6EHwAA7r7A3RfF15OAzmbWrxXLUfFliU0n3N2XA1fS1OSk\nJXFkalniNPrFZbincViG10sxsxqbo8T/P2xFHMVUejkws2OAA4Gj4g6Z2IRndnz9HOEelq1asRwV\nXxZ3n+Xuq929Afgz2f+tFBWncQhwU+OwEq0Xab9qrZurtS6u1nq3WuvYaq1Pq7n+rNb6slrrxrLE\nHVu33AZc5+5/S5Qp1zFj+3gFbyhuyR/QCXibcPN0483Xn8wp80PWvvn65vj6k6x98/XbhJuvC04T\nuIW1O9D4QbF5ZHg5/hf4N9A9Zx4bw5rnNe8MvNf4PsPLMjD+b8CFwPj4/gDWviH+6axvX/Fz3wOu\nrob1kvjcMJp3DHEua3dUcE4p1ksKy7Ev8BrQP2d4f5o6mdiMsCPuk/FlGZh4/RPCPTjrnFYWlyWx\nbh4v9XrRX/v/yrE9FJsmJaqbU4i7JHVxCnGXpN6tdNzxc+2uY8sRd+JzwyhTfZpC3CWrP1OIvST1\nZaXjTnzv7aobyxF33E6vAS7MM7+yHDO2969iM2pVUKHHrzcJZy9OicPOAA6Or7sRKsUphM4jNkt8\n9pT4ucnAfsWmmdhYno7TugXouq55ZHQ5VsVhL8a/xl7fjgNejRvsk8Dnq2CdPAK8DLwCXAtsGIcb\ncEks/zIwIuvLEsc9BuybMyzL6+UGQhOalYR7L46Nw/sCDwNvAQ8Rd7ClWC8VXo4phPtLGn8rjTv5\nr8d18iKhmdxBVbBOJsTv/CVgImtX7HmnldVlieOuAr6XE0NJ1ov+2v9Xpu2h7HVzheMuWV1c4bhL\nVu9WMu447jFKUMeWKe6y16cVjruk9WeFYy9ZfVnJuOO4qyhB3VjquIEvEpowv5TYJvYv9TZeyr/G\ns2EiIiIiIiIiNSmL9/iKiIiIiIiIlIwSXxEREREREalpSnxFRERERESkpinxFRERERERkZqmxFdE\nRERERERqmhJfERERERERqWlKfEVERERERKSmKfEVERERERGRmvb/AbHWlP642X7bAAAAAElFTkSu\nQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c81258750>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:03,333 INFO    : ('rt_wakeup', 'dft_rl5000', 'full_stack')\n2017-06-23 14:28:03,523 INFO    : Found:   189 activations for [2412: rta_rt]\n2017-06-23 14:28:03,525 WARNING : Discarding 1 running times (above 2 x threshold_ms, 0.5% of the overall activations)\n2017-06-23 14:28:03,527 INFO    : 1.1 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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JiLhD0kCqL0+d1RZI+mZkfiVpa9vb5ogfAAAAADCO5Ul8t5d0Z8Xz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\nL5jZ5AKrExGRDm7JqrUMre2ZdhidyTrgVHffEdgDODE+fWE88LC7bws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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8316aed0>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:04,292 INFO    : ('rt_wakeup', 'dft_rl5000', 'ignore_sugov')\n2017-06-23 14:28:04,489 INFO    : Found:   190 activations for [2405: rta_rt]\n2017-06-23 14:28:04,491 WARNING : Discarding 1 running times (above 2 x threshold_ms, 0.5% of the overall activations)\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:04,493 INFO    : 1.6 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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73elOd7rTne50pzvd6U53+sKZ3iuv+EqSJEmSBlqqaq5jmDFDQ0M1PDw812FI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\nNTMzMzMzs6bmiq+ZmZmZmZk1tf8PpHhS6ia1sP8AAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c80c7ba90>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:05,255 INFO    : ('rt_wakeup', 'dft_rl5000', 'rt_mode')\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "2017-06-23 14:28:05,463 INFO    : Found:   189 activations for [2419: rta_rt]\n2017-06-23 14:28:05,464 WARNING : Discarding 1 running times (above 2 x threshold_ms, 0.5% of the overall activations)\n2017-06-23 14:28:05,467 INFO    : 2.1 % samples below 1 [ms] threshold\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3XP1afqw0mo5RN8var/uhF+Xq132B/tLt618n5Rjp5Qrf7ogY9f+23377GE1m\n/H5GIWlaSdcuDaqtNLWm5SlrnvW1ouj8KnWy79tdttv7tVZe1cv2S92slX+R59ZoUn2M8taTbp4f\neeVtP4rMvx/qSd5t7Ifzr91zbazoh/Mo7/HoZlnbPde6rRfHp9119sP+6me92D/V6yyyDN2+/nVS\njmpF3kN0st2ShiJHbEgPbg/k6V2plabW043R1FPTry86anffdLI9LfXENhlqnCevvH8aoN262Um6\ndtXKv8jyjzZF1JPhfPpRdbk6Kedorie9OP/ynmt5jtFoqV+jbZ2jvb5WG6sjHIo8jp3sm37dr72o\n59XrzHt/3g/7sMhy9WsbUw8B7ijSLwFVozTt/Oaz6N+Ktppfv5+0RQw1bmdYMr/f7W/Vx2i4Hg8/\nzKhXT4oKhIsse572I89NRrP8R6r+1ltP3nL1svxFPjho9yFUL7RariKO0Ujti27Wp27l3emDnF6c\nQ71ud6qN1D1jpbF6j9BKW1fk/XMn5Wq3vha9XNfOmTzdvJL2kLRQ0iJJR9WYv5qk89P8ayRNrph3\ndJq+UNLuFdPPlHS/pJur8poq6U+SbpA0JGmHZuXrlyHKjYYtzPj9jNCgXvSvcplmaYa77/PkVa3I\nYQR50rRb1l4MUW5lne3s+3aXbTQcpJNytFqu4c951ldkuVopa6fn1mhXXYfz1pNG5+5IyVuGetuY\nN/8i2uC8imx3Rvr8a7a/WqlP/VC/8uqkXN28vjYykvcCeRR5fWr1/C4iryKN1H1FdT7tlqFar+p0\nLb24fudt/zq9lna7jWz3+jqsnXuIIstVc9mcQ5TzBLfjJN0q6dWSVpV0o6QpVWk+Luk76fN+ks5P\nn6ek9KtJ2izlMy7N20XSG2sEuJdJ2jN9foekOc3KOBIBbpG/c6yVrl7D1E7lGp5XZECVp6ztTMuT\nVye//6rOr919kyfvVtQrV7P8u/07ym4EFpWK/C1LnrJ2sl/z6PZyndbzRmkqpxXRVrSrnXLVK2er\n291qufK2RUXmn+f868Vvo1o9RiN9g1qpUT0uolx5zr8i19nJ8W6lrK2m6/RmvVsPcoq+r8izXN7t\nLjooHcmH8PXK0I5O2tZ27yvavb620/a1UjfbvUdpdn/bqFztXrPaLWsneRUZ4L5Z0qUV34+WdHRV\nmkslvTl9Hi/pAUmuTluZLn2fXCPAvVTS+9Ln/SWd26yMIxHg5tnxndz457mJKXpanjR5GphOGqZO\nb8ybaTchmOQyAAAgAElEQVQYz6PTC1GzvDqpT+1eqNut5+0et3aPYyf5t9uwFhksF31s211nkcFZ\nu9ptPzq6QLZZzxtNG40PFCunt3rzU+T5V69MecqeR7frdJH3At2+OSyyPW+3jaylm/uwk/uKIvdh\nu+1aLUXmVUueZfN2GLS7D4tsd2ppt+4Xed/d7Yemo6mtyH2eFhjg7ivp9IrvB0o6pSrNzZImVXy/\nVdL6kk6R9IGK6WdI2rfie60A93WS7pB0p6S7JG3arIy9CnCHT+BOT+xa+Rfdo9du/rW022h2+6TN\no9s3XO1Oa/cmuV7+RR6jdtdX5AW4Fw1yNy9qnWx3N49trfw7Od7dDFKKPJdrLdvJTXGR7VO1oq8N\n7eY10g8h2g0G8y7bSd1pZ3310hV5jLp9U9xufc1Tjl7sw14EVHnanUbb3eieocj2sFa6bu/DXtxX\n9OKBSR7dPh551tlJe1voPfsoDnBPlvSe9Pm9kn5bp1wfS7/RHdpkk03a2nHNtPpUu9WDWsQT/7x5\ndRpQtRN4dbLd9dK0U4Z2HkLUUuRFM0+52m0QOl226PXlPZaNjkMr21NUna53frd73LpVrqK121a0\nexPTbrny9h7k1epxK+oYtVv+dpZrtw3Lq9Cn9DnqTq1peetF9bKdXgfaOT9aedBS5PHOW9aRbIvy\nHI9292Fl2iLO5eH57R6PVpfNW/eLrNNFPsBqdR92cp1sd7lO63QnbV2jPJuVtZ372yLveVspa2F5\njeIhyg9LcvpsSY80K+NI9uC2+rQs70Ht5oUib5oylrWo5fIu225Zi9jGIh9CdGt9eS6QneTfbH3N\n0rV60SzyhqWdcnXr2NYqQxHTimw7aun2OZN3u4t8St/pckU+SO2WTs+/Wnm1M62Vsraapt1jVGRe\njcpaVNDbiiLbzSLP5VYDr07K1Up96lZw1sr1KG9AVcQ+bPU62e5yI3l9LfIYdeP6WuR9V7fu4YoM\ncMdLuk3ZS6KGXzK1dVWaQ/XCl0xdkD5vrRe+ZOo2pZdMRf0Ad4Gk6enzbpLmNitjL4coV+rGk9ai\nypqnXO02Jp3oxs1Cp+urlOdYdvIUslG5OmlcizxGRa6v0wtRqzrJP09Z262bRZarGzq92e3kXOhE\nJ/tmpNuidsvQyXIj3S400ur1KO9NU6NjNFK95Z2sr912J09eedMU2dbl1e59Syt5NUvT7rRutOdF\njQioJ+9+bfXhSy1FXl/z5N9v19eRChqLLnuR16NC8yoqwM3y0jsk/TUNPT4mTTtW0t7p8+qSfqzs\nzwFdK+nVFcsek5ZbqPR25DT9PEn3SHpG0hJJH0nT3yJpbgqMr5G0fbPy9eotykXeyBZ5s5cnr04a\nx5EuaydDLIpaLqK7N8BF3+yP9I1su/W8G0Nl2sm/3eFY7bYLRZar2zrZ7nZvPNrV7fO7yLYob/5F\nLtdPAW6lTm+A2xmm182HCZ2sr/JmtZXt6XZZu90WFXk/VeS5nKdudnI82r1fG+l9XytdkYFkJ21r\nu/cVvbi+Fnkv0249zKubZe0oryID3H7/16u/g9tuw9QP+rVc/aybAW7RRnqYYbfX14thk0VeNIs0\n0vui20/p+0Uv6thI69dtzHPj327w0co6+2G56u3s5Jzp9gOTfngw325eefXDA/ZOe2vbyT9Puk7q\nSb+2Rd0uVy8eTLSrHx7e1kKA2yfG6klcRiPd2wz0gyJvYjgXUEuRAUO3g6B+CG7GqrHafvTLQ9Ox\nuv+L1K9B42iSN8AdfpnTqDYwMBBDQ0O9LgYAAOihwTmDGpw+2OtiFKZs2wMAnbA9NyIGmqYjwAUA\nAAAA9LO8Ae4qI1EYAAAAAAC6jQAXAAAAAFAKBLgAAAAAgFIgwAUAAAAAlAIBLgAAAACgFAhwAQAA\nAAClQIALAAAAACgFAlwAAAAAQCkQ4AIAAAAASoEAFwAAAABQCgS4AAAAAIBSIMAFAAAAAJQCAS4A\nAAAAoBQIcAEAAAAApUCACwAAAAAoBQJcAAAAAEApEOACAAAAAEqBABcAAAAAUAoEuAAAAACAUiDA\nBQAAAACUAgEuAAAAAKAUCHABAAAAAKVAgAsAAAAAKAUCXAAAAABAKRDgAgAAAABKgQAXAAAAAFAK\nBLgAAAAAgFIgwAUAAAAAlAIBLgAAAACgFAhwAQAAAAClQIALAAAAACgFAlwAAAAAQCkQ4AIAAAAA\nSoEAFwAAAABQCgS4AAAAAIBSIMAFAAAAAJQCAS4AAAAAoBRyBbi297C90PYi20fVmL+a7fPT/Gts\nT66Yd3SavtD27hXTz7R9v+2ba+T3Cdt/sT3f9jfb2zQAAAAAwFjSNMC1PU7SqZL2lDRF0v62p1Ql\n+4ikhyJic0knSvpGWnaKpP0kbS1pD0mnpfwk6aw0rXp9b5W0j6TtImJrSd9qfbMAAAAAAGNNnh7c\nHSQtiojbIuJpSbOUBaCV9pF0dvp8oaTdbDtNnxURT0XE3yUtSvkpIv5X0oM11vcfko6PiKdSuvtb\n3CYAAAAAwBg0PkeaiZLurPi+RNKO9dJExArbD0taL03/U9WyE5usb0tJO9v+qqQnJX02Iq5rtMDC\nZQs1/azpL5i215Z76bM7fVaSXjSP+cxnPvOZz3zmM5/5zGc+85k/eubnlSfAHWnjJa0raZqkN0m6\nwParIyIqE9n+mKSPSdJqE1cb8UICAAAAAPqLq+LGFyew3yxpMCJ2T9+PlqSI+HpFmktTmqttj5d0\nr6QNJB1VmbYyXfo+WdIvI+L1FXldIukbEfH79P1WSdMiYmm9Mg4MDMTQ0FBrWw4AAAAAGBVsz42I\ngWbp8vwG9zpJW9jezPaqyl4aNbsqzWxJB6XP+0q6PPW4zpa0X3rL8maStpB0bZP1/UzSW9NGbClp\nVUkP5CgnAAAAAGAMaxrgRsQKSYdJulTSAkkXRMR828fa3jslO0PSerYXSfq0Vvbczpd0gaRbJF0i\n6dCIeFaSbJ8n6WpJW9leYvsjKa8zJb06/fmgWZIOqh6eDAAAAABAtaZDlEcDhigDAAAAQHkVOUQZ\nAAAAAIC+R4ALAAAAACgFAlwAAAAAQCkQ4AIAAAAASoEAFwAAAABQCgS4AAAAAIBSIMAFAAAAAJQC\nAS4AAAAAoBQIcAEAAAAApUCACwAAAAAohfG9LgAAACPhmWee0ZIlS/Tkk0/2uiiosPrqq2vSpEma\nMGFCr4sCACiBMR/gDs4Z1OD0wV4XAwDQZUuWLNFaa62lyZMny3aviwNJEaFly5ZpyZIl2myzzXpd\nHABACYz5Icozr5jZ6yIAAEbAk08+qfXWW4/gto/Y1nrrrUevOgCgMGM+wAUAjB0Et/2HYwIAKNKY\nDHAH5wzKMy3PzC6qw58H5wz2tmAAAAAAgLaNzQB3+qBiRihmhCQ9/5nf4gIAqo2Wh58f/ehHdcst\ntxSW3+LFi3Xuuec+/31oaEiHH354YfkDANANYzLABQAgr269qyEi9NxzzxWW3+mnn64pU6YUll91\ngDswMKCTTz65sPwBAOiGMRfgVj+Jn7HrjKZpAAAowuLFi7XVVlvpgx/8oF7/+tdr3Lhxz8+78MIL\ndfDBB0uSDj74YB1++OHaaaed9OpXv1oXXnihJGnOnDmaPn269t13X732ta/VAQccoIhsNNL06dM1\nNDQkSVpzzTV1zDHHaLvtttO0adN03333SZJuvfVWTZs2Tdtss42++MUvas0116xb1qOOOkpXXnml\npk6dqhNPPFFz5szRXnvtJUkaHBzUQQcdpJ133lmbbrqpfvKTn+jII4/UNttsoz322EPPPPOMJGnu\n3Lnaddddtf3222v33XfXPffcU+wOBQCgSqkD3FqBavWT+FrDknmzMgCMbd18V8Pf/vY3ffzjH9f8\n+fP10pe+tG66e+65R1dddZV++ctf6qijjnp++p///GeddNJJuuWWW3TbbbfpD3/4w4uWffzxxzVt\n2jTdeOON2mWXXfT9739fknTEEUfoiCOO0Lx58zRp0qSG5Tz++OO1884764YbbtCnPvWpF82/9dZb\ndfnll2v27Nn6wAc+oLe+9a2aN2+e1lhjDf3qV7/SM888o0984hO68MILNXfuXH34wx/WMccck3c3\nAQDQllIFuNU3HgSqAIB2dPNdDZtuuqmmTZvWNN273/1urbLKKpoyZcrzPbCStMMOO2jSpElaZZVV\nNHXqVC1evPhFy6666qrP97Zuv/32z6e5+uqr9W//9m+SpPe///0dbceee+6pCRMmaJttttGzzz6r\nPfbYQ5K0zTbbaPHixVq4cKFuvvlm/cu//IumTp2q4447TkuWLOlonQAANFOqALdeQJvnSTxvVgYA\njITKXtvKP5FT/bdgV1tttec/Dw9Drp4+btw4rVix4kXrmDBhwvN510vTqeFyrLLKKi9Y3yqrrKIV\nK1YoIrT11lvrhhtu0A033KB58+bpsssuK7wcAABUKlWAK9UOVGdeMVMzdp3R8Ek8b1YGANRS610N\nRXnlK1+pBQsW6LnnntNPf/rTrq1n2LRp03TRRRdJkmbNmtUw7VprraVHH3207XVttdVWWrp0qa6+\n+mpJ0jPPPKP58+e3nR8AAHmUIsCde/fc5wPa4V7c4RsSAlUAQCe6ef04/vjjtddee2mnnXbShhtu\n2LX1DDvppJN0wgknaNttt9WiRYu09tpr10277bbbaty4cdpuu+104okntryuVVddVRdeeKE+//nP\na7vtttPUqVP1xz/+sZPiAwDQlCuHPY1WAwMDMTQ0JM/08z2wkl70Xcp6eJvdrORJAwAYXRYsWKDX\nve51vS5GT/3jH//QGmusIduaNWuWzjvvPP385z/vdbE4NgCApmzPjYiBZunGj0RheqXmnwDKEbgS\n3AIAymju3Lk67LDDFBFaZ511dOaZZ/a6SAAAFKpUAW51QEugCgDASjvvvLNuvPHGF0ybN2+eDjzw\nwBdMW2211XTNNdeMZNEAAChEqQJcAloAQCMR8YI3FyP7sz433HBDz9Zfhp9KAQD6RyleMgUAQDOr\nr766li1bRkDVRyJCy5Yt0+qrr97rogAASqJUPbgAANQzadIkLVmyREuXLu11UVBh9dVX16RJk3pd\nDABASRDgAgDGhAkTJmizzTbrdTEAAEAXMUQZAAAAAFAKBLgAAAAAgFIgwAUAAAAAlILL8DZJ20sl\n3d7rcmBErC/pgV4XAqVCnULRqFMoGnUKRaNOoWgjUac2jYgNmiUqRYCLscP2UEQM9LocKA/qFIpG\nnULRqFMoGnUKReunOsUQZQAAAABAKRDgAgAAAABKgQAXo833el0AlA51CkWjTqFo1CkUjTqFovVN\nneI3uAAAAACAUqAHFwAAAABQCgS4AAAAAIBSIMBF37I9zvafbf+yxryDbS+1fUP699FelBGjh+3F\ntuel+jJUY75tn2x7ke2bbL+xF+XE6JGjTk23/XBFO/XlXpQTo4ftdWxfaPsvthfYfnPVfNoptCRH\nnaKdQm62t6qoKzfYfsT2J6vS9LydGj/SKwRacISkBZJeVmf++RFx2AiWB6PfWyOi3h8h31PSFunf\njpL+O/0PNNKoTknSlRGx14iVBqPdtyVdEhH72l5V0kuq5tNOoVXN6pREO4WcImKhpKlS1hEl6S5J\nP61K1vN2ih5c9CXbkyS9U9LpvS4Lxox9JP1PZP4kaR3bG/a6UADGBttrS9pF0hmSFBFPR8TyqmS0\nU8gtZ50C2rWbpFsj4vaq6T1vpwhw0a9OknSkpOcapHlPGvpwoe2NR6hcGL1C0mW259r+WI35EyXd\nWfF9SZoG1NOsTknSm23faPvXtrceycJh1NlM0lJJP0g/zznd9kur0tBOoRV56pREO4X27CfpvBrT\ne95OEeCi79jeS9L9ETG3QbJfSJocEdtK+o2ks0ekcBjN3hIRb1Q2dOZQ27v0ukAY9ZrVqeslbRoR\n20n6L0k/G+kCYlQZL+mNkv47It4g6XFJR/W2SBjl8tQp2im0LA1331vSj3tdlloIcNGP/knS3rYX\nS5ol6Z9t/6gyQUQsi4in0tfTJW0/skXEaBMRd6X/71f2e5EdqpLcJalyJMCkNA2oqVmdiohHIuKx\n9PliSRNsrz/iBcVosUTSkoi4Jn2/UFlwUol2Cq1oWqdop9CmPSVdHxH31ZjX83aKABd9JyKOjohJ\nETFZ2fCHyyPiA5Vpqsby763sZVRATbZfanut4c+S3i7p5qpksyV9ML39b5qkhyPinhEuKkaJPHXK\n9qtsO33eQdk1d9lIlxWjQ0TcK+lO21ulSbtJuqUqGe0UcstTp2in0Kb9VXt4stQH7RRvUcaoYftY\nSUMRMVvS4bb3lrRC0oOSDu5l2dD3Xinpp+kaPl7SuRFxie1DJCkiviPpYknvkLRI0j8kfahHZcXo\nkKdO7SvpP2yvkPSEpP0iInpVYIwKn5B0Thr+d5ukD9FOoUPN6hTtFFqSHur+i6R/r5jWV+2UqcMA\nAAAAgDJgiDIAAAAAoBQIcAEAAAAApUCACwAAAAAoBQJcAAAAAEApEOACAAAAAEqBPxMEAECP2V5P\n0u/S11dJelbS0vT9HxGxU08KBgDAKMOfCQIAoI/YHpT0WER8q9dlAQBgtGGIMtpi+zu2v9TrcnSL\n7fNsv7vX5WiH7Z1tL2wwf7LtsF1zBIftL9g+vXslbI/txbafsP3DLuR9ue0nbV/VwjKDtn9UdFlq\nrKfh8Wqy7HTbSxrMP8v2cenztrb/2ElZ0R22H0v/T7d9he2f277N9vG2D7B9re15tl+T0m1g+yLb\n16V//9TbLUC3NWuPbM+3PX0Ei9QTqe7/xfYaXch7ju2P1pm3ie3HbI8rer0jwfbBtp9N2/C6gvN+\nW8r3OdtvKzLvXujkmtzGuurWuRzLLq63v6vvDdI1ZOt2y9mPCHBLouLm/zHb96Yb1zULyvvg6hv/\niDgkIr5SRP451v8y2yfZviNt363p+/pp/vC2P2p7ue0/2j7E9ioVeZxl++m0/PC/99VZ37aStpP0\n8/T9nbavSnnfa/t022s1KO9X0s3mitQTUzlvu3Sj8YDtT1dMn2D7Gtsbd7SzJEXElRGxVUXedRu5\nOst/LSLabVDfavv3th+2vThH+pfYPi3tj4dt/2+TRd4VEQe2U7ZGIuKfJR1Sb36zQLEMIuImSctt\nv6vXZUFD2ymrq6+TdKCkLSNiB0mnS/pESvNtSSdGxJskvSfNQ5VW28aK5dq+6eyViNg6IuY0SjOS\nN+5ddJSksyLiieEJKcC63vbjtpfYfm/1QrY/mLa9reMaEXdExJoR8WwHZW/K9vdsL0zB4sE50jfd\n9gpXp21YUFyJpYj4bUSsKemOIvMdKe22E6PMtyQd2+tCFIkAt1zelRqRqZLeIOnoHpenY7ZXVfa7\ntK0l7SHpZZLeLGmZpB0qkr4rItaStKmk4yV9XtIZVdl9MzXew//Or7Paf5d0Tqwcv7+2pOMkbaTs\npnKipP9sUOxFko6U9Ksa874u6bPKblKPsf2qNP3Tki6KiDsb5DsaPC7pTEmfy5n+e5LWVbZf15X0\nqS6Vq6dG0Q3jOcrqP/rXdRFxT0Q8JelWSZel6fMkTU6f3ybpFNs3SJot6WVFPfAE2tXtdtD2apIO\nkvSjimlTJJ0r6Rhl1/LtJM2tWu7lkr4gaX43y1eQGyV9XNL1zRLm2fbRaBRdTyWNmvLOlvTWinvS\nUY8At4Qi4l5JlyoLdCW9+Ilzda9senJ5iO2/pZ7KU515naTvSHpz6vVcntJXDm2cnp4MHmn7ftv3\n2H637XfY/qvtB21/oWJdq9g+KvXELrN9ge1162zOByVtIulfI+KWiHguIu6PiK9ExMU1tv3hiJgt\n6X2SDrL9+jZ24Z6SrqjI89yIuCQi/hERD0n6vqS6Q/4i4uyI+LWkR2vM3kzS5RFxl6S/SdrE9qbK\nellObFQo22fb/kz6PDEds0PT99ek/bxKZW+js+G8m0j6RTp+R1ZkeYCzXvEHbB9TsZ7nh7pVPNE/\nqFbaGtt+bUT8UNJtjbYl5f1aSXtL+lhELI2IZyOipYtvKtvHU7191Fnv+Wuc9eI/kurWqint+rZ/\nmer3g7avdEUvf4N1vFTSryVt5JW9/xul2ava/p+07vm2ByqWW2z787ZvkvS47fG2N3I2dHSp7b/b\nPrwi/Q62h1K577N9QlVR6h2v1ZyNaLg7/Tsp3ejV2pY3OHua/6jt8yWtXpVkjqTd6i2PvvBUxefn\nKr4/p5UvjlxF0rSImJr+TYyIx0aykKOZ7ZentmKp7YfS50lp3lcl7azsAcJjtk9J019r+zepbVno\nip6ydL081fav0rl3jdNw8jR/64pl73P2M5FX2f6Hs5ePDad7YyrThDpFb9YevS19rtfWDI+gWZ62\n7c3pmvJF27c7u77/j+21K/L9YJq3zPaXqtYzaPtC2z+y/Yikg9O6r07t8D22Txluo9Myudv0GnaU\ntDwiKkfbfFHSdyPi1xGxIiKWRcStVct9XdLJkh6ok2+l1zgbzvmIs58KrJvK/YLeb2f3XF+x/Ye0\nHZd55aiz1dM+WZb2w3W2X5lj3YqIUyPid5KezJE8z7bXlY7fj1NZH3U2Mm1L20enunCn7bdXpD/Y\n2U8nHnV2fTughXVtZvt/07K/TedL9X3IR2zfIenyNH1aqhfLbd/oiiH4tte2fUaqY3fZPs5p+Hgq\n51W2v+Xs/P677T3rlKvde6jqel/3vjdHfdi0Vj1Ky+7t7FxfnupczeHlttdw1g49ZPsWSW+qnB8R\nTyp7+LF7s2M1WhDglpCzC/GeynoSW7GXskq/raT3Sto9DVU5RCuHrqxTZ9lXKbtZnijpy8qCwA9I\n2l7ZzcCXbG+W0n5C0rsl7aqsV/QhSafWyfdtki5p9eYsIq6VtCStOzdnwcxmkur+hlXSLmr/Se/N\nkt6ejtFkZT0w35b0uYh4psmyV0ianj7vqiyI3KXi+5UR8VzlAmk47x1KvfsR8c2K2W+RtJWk3SR9\nuV7D2EbavHaQdLukmekCMc/2e9rIZ3dl9Wyasp7z7ymrextLer2k/VO6zyirExtIeqWyJ/ZN37IX\nEY8rO5/uruj9vzvN3lvSLEnrKHsCekrV4vtLemea/5ykXyh7Aj9R2b78pO3hC8q3JX07Il4m6TWS\nLqjKq94xOCZt+1RlT+h3UHZj8wLppvBnkn6orLf8x8oerFRu612Snknrweh1mVYOV5btqQ3S4sVW\nkfQDZSOCNpH0hNK5HRHHSLpS0mGpLTgsXTd+o6y37BWS9pN0mrMetGH7SZop6eXKrs1flSRnP3f5\nraRLlF0PN5f0u/Sgeo6ya/GwAyXNanCtaNYeDavX1gxfT9ZJ23a1pIPTv7dKerWkNYfzTdt3mqQD\nJG2orJdwYtW69pF0YSrTOcreDv4pSesrG421m7IeyUp52/Rq2+jF1+5pqazzUrDzI1c8ULe9g6QB\nZQ/y8/igpA8r294VygLjet4v6UPK6sSqykZvSVkv89ppe9ZTdo/1RK0MOtRw23N6l7Jrxssl/VlZ\n58kqyo7zsZK+m9bxUmX7Ys80mm4nSTe0sJ5zJV2rbH8MKqvr1XZVNtprd9sTlY2SO07Z9eyzki6y\nvUFKe5ay47O5shGNb5dUOfx8R2V1ZX1J35R0hm1Xr7CDe6jqet/ovrdZfahZj2xvKek8SZ9Udl9z\nsbJAvNYDoBnKzvXXKDu/DqqRZoGye4hSIMAtl5/ZflTSnZLuV1ahW3F8RCyPiDsk/V4VPcA5PCPp\nq+nCO0tZo/HtiHg0IuZLukUrT5xDJB0TEUvSMLtBSfu69jCO9STd0+J2DLtbWcM37LPpKddy2/We\n1A4H8LV6X2X7X5Q1DF9us0yflfQfym4+PqWsJ/hRSX939jT4Ctv/VmfZKyS9xVmv4y7KGuXhnuRd\nVdHrnNPMiHgiIm5UFnQ1athaSZvXJGU3Kw8ra/APk3R2G8HzNyPikVTPbpZ0WUTcFhEPK+t5fUNK\n94yym5JNI+KZ9FvlTl8jf1VEXJx+d/VDvXi/nBwRd6bfg71J0gYRcWxEPB0Rtyl7ELRfRfk2t71+\nRDwWEX+qyqveMThA0rFpZMNSZTfRtW4OpkmaIOmktP0XSrquRrpHtfI8wOh0uKQB2zelp/V1f1+O\nF0s9XRelUTuPKgtGd22wyF6SFkfED1JP2Z8lXSSpsi3/aRrhskLZDe/UimXvjYj/FxFPpmvmNWne\n2coCO6Xep/2VtTP1NGuPhjVrayodIOmE1KY+puynT/ul6/W+kn4REVdFxNPKrovVberVEfGzNPrq\niYiYGxF/SvtpsbIAqXrf5m3Tq62jF1+7JylrD98jaQtJa0j6L+n5fXqasocVzymfH0bEzenB55ck\nvdf1Xyz1g4j4a2r/L9DKY/6MsnubzYdHLkXEIznX34q6296CKyPi0lRvf6wskDq+4l5vsu3h68Vz\nkl5ve43IfkaRqyPA9ibKro9fTtfGq5TdI1UbjIjH0/78gKSLU31/LiJ+I2lI0jtS7+c7JH0ypb9f\n2Qi5/Sryuj0ivp/OlbOV3Rvk6kWv0Oi+6AX1Xo3ve5vVh3r16H2SfhURv0nH41vKjnGtPyn3XmX3\n6A9G9lO4Wg9mSnXtJ8Atl3enJ2fTJb1WWZDZinsrPv9D2ZPavJbFypcrDD95uq9i/hMV+W0q6afD\nwaayp0bPqnbjskxZw9OOiZIerPj+rYhYJ/2rt2+Wp/9f9BIp29OUPWXcNyL+2k6BIuL2iHhHRLxR\n2UusvqIs6P2WpPOVPYE/odZT1siGFj2urHHbWdIvJd1teyu1F+C2crw7qRv1PKGsYT8uXdSuUPZg\n5e2NF3uR6npWr979p7Kek8ucDaM6qr1iv0D1flm96kFN5W+qN1U2zHl5Rd3/glbW+49I2lLSX9IQ\npb2arGt4uzZS1hM+7PY0rdpGku6qCupvr5FuLa08D9ADETEYFX8iKLJ3Kygi5kTEXhXTp0fEUPW8\niEeuQikAACAASURBVHggIt4XEdtGxJSIIMBtgbOX333X2dDbR5QN3V2nQSCzqfT/27v3MLvK+tDj\n399M7iQkQrgTBBQvHLRCo3jpBUVaRAX1WAtHrVqOObUF5ag9BvWx0VNb8NQL+lglKoJYuYhtRaVS\nFcFLKzelyC0aQUwgXCSQBHKZTOZ3/njXkM1kZrKTzF5rZs/38zzzZO1127/9Zu39rt9a7/sujh7y\n3X49pWXToJG+vwsoLXmG83Xg8Kr103HAmiytk0ayvd+jQdv7rWk13O/LFMrv1v60/MZl5npKnd3q\nCeNKRGni+s0oAzauBf6Obc9V2v1NH+phtq27N7A1QXi0er8TqmV/Cdy8nQR/qNbPczflouFI5xMj\n/Z9fSLkTenGUbiUfiZGbne+K0T57u4aW/W+HOdebXSX8f0pJ5FZFaY7/jDbfY39gdXX8DBpuPJKh\n9emfDPnO/R7VRWzK/8uqlmXnUu6ADnr8/6blfXf0vGa086Kh8Y923ru946Gtur+6SLOCbVtRDK47\n9NgdqqvqfhPcLlQlCudTkqZBjwGzWl7vSEfysX5Y8gpKM5Z5LX8zsjSPHOq7lOYou+3IG0TEcylf\n8rYf+wKPN0f9FaXyb93fkZQrin+epf/LWPgA8LnMvJ/StOqG6gr1SkqzmuFcQ7lqPq0qr2sod5Sf\nxMjNgcbrw65vHmZex2Kt7oy8KzMPpVxIeGdEHNvu5jv7ti3TK4C7hhz3czLzhCq+X2bmKZRK+Gzg\nsjaP+3spleegg6p5Q60CDhjSDOug1hWqZl/TGL2JvtTt3kVpenh0lma8g013B787Q38PVgDXDPlu\nz87Mt7XxXisoTX+3kaVf3KWUu1VvZPS7t20b5bdmuN+54X5f+imJzyrKXUKg9POj3Il6wtsNef0Z\n4A7gsKps38vWct1VNzOk7q7mtcbQOn0s8Ooq2b6Pcufro1H1qx5B65MODqJcpG2n7+7WAEoLmg9m\n5uHVe76C0vR5rI322cdcdaf3OEqSeQelhVI7VgF7RETrOepwT5QYWp9eOOQ7t1tmnlUt2wTMb1m2\ne2bu7GNwdqbchvuNGPa8dxeOhyd8N6u6fQEw3Ln0KrY9dod6JuVOdFcwwe1enwCOi4jBJhM3Aa+p\nrkw/lXIFt133AweO0K5/Z3wW+HCUwZWI8ty6k0ZY90LKD8PXogzi0RMRe0YZhGObK5FRHin0CkrT\nmS9n5s93Ir4raGkyFWWgqm8Dp2fmN7a3cZRH/sygfL+mRBlAoHfIOodT7rR/ppp1F/CSqmnNYYw8\nnP41lKa8g4OBXF29/lGO/HiC+xnhBGqsVf8/MyhXT6P67CMdNz+gfM4zowzA9CJKP68rOxTbKyLi\nqVUlsIZy9bTdZmn3A3tGy+AqO+E6YF2UgadmRkRvRBxRXYwhIt4QEXtVV2EHr6K2E99FwPur79F8\nyoWT4Z6H+Z+UE9O3V8foa3jiSORQjvurqiZU0mQwtfqdGvybQrmTsYEy2NIebNvdZ+hv6jeBp0XE\nG6vv1tSIeG6b3S2+CewXEWdEGTBuTkQc3bL8S5Q+sCcyRgnuKL81D1b/tn62i4D/HWUQoNmUu4CX\nZGmyehnwyoh4YfU7v4TtJ6tzgLXAo9UdvnYuArTrOsqd9tY7WF8E3hIRh1YJ1GJKmUMp12dSWkU9\nh9LE9YOUcQ1G8oaIOLza14eAy0ape4cV5XF6z6rOC9ZSkuSBatmSiLh6lG2nVXVssPXYHelcfrTP\nPqYiYp+IOKm6ULIJeJQ269fMvJtS9kuqz/cCSt/f0XyZcuz9cVWXzogywOaBmbmKMg7BR6tzwp4o\nA5WN1s1gNGNxDjXiee9ox8N2XAq8PCKOre74votS9sM9z/5SyrnWk6KMAXN668LqmPpdylgCXcEE\nt0tl6Yv3Jbb2Ff040Ef5ol5A6QPUrqsogyrdFyP3Xd0R51Duhv57lD7DP6F0+N9GdaL9UsrVwO9Q\nvvzXUZoEXduy6jdia//j9wEfo3TK3xlLKaPjDVbU76L0O/lCbB1F9/G+JRHx2YhoHaDic5STo1Oq\nWDawbZ/ITwPvaKkYz6T0m7sV+LssA4wM5xrKCcJggvsjyp350Z4f+/eUBOiRiHj3KOuNhT+gfN4r\n2Do4y+BjTIgy2t/roVzFpgzEcAIl4fwc8GeZeUeHYjuM0iLgUUqy94+Z+f12Nqxiugi4syrH4ZoA\nb28fWyhXZp9DuaDxW8rzSQeT5uOBWyPiUcp35ORseZbjKP6WcnJwM+VRMT+t5g19/z7gNZSTutWU\n5mT/PGS119P+YCtSN7iC8js1+LeEcoF4JuU7+hPKBc5W51D6zz0cEZ/M0k/3jyh9/O6lNCk8G9ju\naOTVtsdRTujvo4yu/+KW5T+mnOz+tEoExsKwvzVVU80PAz+ufueeT3ns24WUOuYuyui9p1ex3VpN\nX0y5Q/QoZfyP0S6QvZsyaM46ym/+SI/r22HVb9z5VP2Wq3nnUc6FrqU0y9xEqWvJMubIfYN/lHOk\ntVVLqpFcWL3HfZSBNd8+yroj2ZdycWAtpanqNWy9eLEA+PEo2/475Th9IeVcZQNVC4OIeH3ruclo\nn70DeiiPO7yXUr/8ITt28eL1bH0E5N9SjosRj6Ms/UhPorQAeJBy7vfXbM1r/ozSGuk2StP1y9j5\n7m5jcQ412nnvaMfDiDJzGeVY/xTlt+qVlMGw+oZZ/YOUY+AuyjE0dP+vBK7OrQNoTniRuzzGitR9\nIuIrwKWZ+a9Nx6IiIpZRKqh/yczhRgDclX1/hzII03WZ2W6z5a4SEc+mPFLiBU3HImmriLgK+Epm\nfr7pWEZT3eF9hNL8+K6GYtiLMsr1kW1eIBxXojy7+tjMHNqXudPv+0ZKP9U+4AVZnqAxVvs+ljLo\n2nTghHYuLEd5jN0dmbmjg6VqJ0TEtcCpmXlL07GMFRNcSZKkcajqwvAdYEF1t3dciYhXAt+jNJn9\nKOWu1FHpyaV2QHWcr6bcYfwjyiPtXpBlRHJph9lEWZIkaZyJiAso3SrOGI/JbeUkSrPUeyndQE42\nudVO2JcypsijlEfYvM3kVrvCO7iSJEmSpK7gHVxJkiRJUlcwwZUkSZIkdYUpTQcwFmbNnZXz9p3X\ndBiSxpn95+zw03wkAG688cbfZuZeTccxkVk3SxOTdafGq3br5q5IcOftO49F5y5qOgxJ48ySY5Y0\nHYImqIgYq2eOTlrWzdLEZN2p8ardurnWJsoRcV5EPBARwz5nKYpPRsTyiLg5Io6qMz5JkiRJ0sRV\ndx/c84HjR1n+Msow84cBi4DP1BCTJEmSJKkL1JrgZuYPKA9yHslJwJey+AkwLyL2qyc6SZIkSdJE\nNt764B4ArGh5vbKat6qZcCRJE11mMpCwZSAZyCQTBjKrv7K8LCvTbzn/+qZDblREzAM+DxwBJPDn\nwDLgEuBg4NfA6zLz4YZClCRpROMtwW1bRCyiNGNm7j5zG45GkprX1z/Amg2bWbOhj4fXb+aR9Zt5\nZH0fazZsZlP/AANVErc1udv6OpMRl2cmAwNl/pYnJIiD25blWwanW5ZvTR5b9tW6/TbLWpYPDImv\ndfnAMPEPLh944rraYecA387M10bENGAW8F7ge5l5VkQsBhYD72kySEnd5eDF3wLg12e9vOFINNGN\ntwT3HmBBy+sDq3nbyMylwFKA/Z++v6cwkiaF9X393LTiEZbdt45l963jjvvW8eC6TTyyvo/H+ra0\nvZ8I6ImgN+Lx6Z7q3wjo6RlctnV+T1Be90BvxNZ1W6Z7e+KJ67Ys7+mBnp6eIdtV//Y8cd3H4+sZ\nLr4yPfhewy3v7Xniuq0x9fZsu6/WGKb0BCef3cH/xHEsIuYCfwC8GSAz+4C+iDgJOKZa7QLgakxw\nJUnj0HhLcC8HTouIi4GjgTWZafNkSZPejXev5lNXLec/fvUQff0DAOyx2zSevs8cnn/onjxp1lTm\nzZrK3FnTmDezTM+bOY15s6ay+8ypzJza+4QENiIa/kTj28lNB9CcQ4AHgS9GxO8ANwLvAPZpqY/v\nA/YZbmNbV0mSmlZrghsRF1GuAM+PiJXA3wBTATLzs8AVwAnAcmA98JY645Ok8WjF6vW87cs/ZcPm\nLbzh6Cfz+0+bz3/bf3f2mj3dRFVjbQpwFHB6Zl4bEedQmiM/LjMzIoZtOWXrKklS02pNcDPzlO0s\nT+CvagpHksa1zOTsby/jvB/dxfSpPVz01udzxAHeFVNHrQRWZua11evLKAnu/RGxX2auqp5u8EBj\nEUqSNIq6n4MrSWrTlbfez2ev+RXHPnNvrnj775vcquMy8z5gRUQ8vZp1LHAbpQvRm6p5bwK+3kB4\nkiRt13jrgytJk1ZmcvdD67nl3jXccs9azvvRXRxxwO584uTnMH1Kb9PhafI4HfinagTlOyndhXqA\nSyPiVOBu4HUNxiepizmasnaVCa4kNWTzlgHufmg9dz74KLfcu5Zv3nwvdz74GABTe4Nn7Ls7H3nt\ns01uVavMvAlYOMyiY+uORZKkHWWCK0k1yUxuuPthvnXzKn74ywe5+6H19FcPao2Aow/Zg7e88GCO\nPOhJHLbPbBNbSZKkHWSCK0k1Oevf7uDcH9zJtCk9vOgpe3L8Efty6PzZPGXv2Ry6127sPmNq0yFK\nkiRNaCa4klSDNRs2s/SHd3LSc/bnw69+FrOn+/MrSZI01hxFWZJqsHbDZjLh95463+RWkiSpQ0xw\nJUmSJEldwQRXkmqwZsNmAGZMdeAoSZKkTjHBlaQaPLBuIwAL9pjVcCSSJEndywRXkmqw+rFyB3f2\ndO/gSpIkdYoJriTV4KFHNwGw39yZDUciSZLUvUxwJalGEU1HIEmS1L1McCWpBo9t6icCesxwJUmS\nOsYEV5JqcO+ajey7+wxHUZYkSeqg2hPciDg+IpZFxPKIWDzM8oMi4vsR8bOIuDkiTqg7RknqBO/e\nSpIkdVatCW5E9AKfBl4GHA6cEhGHD1nt/cClmXkkcDLwj3XGKEmSJEmamOq+g/s8YHlm3pmZfcDF\nwElD1klg92p6LnBvjfFJkiRJkiaoKTW/3wHAipbXK4Gjh6yzBPj3iDgd2A14aT2hSZIkSarTwYu/\n1XQI6jLjcZCpU4DzM/NA4ATgwojYJs6IWBQRN0TEDevXrK89SEmSJEnS+FJ3gnsPsKDl9YHVvFan\nApcCZOZ/AjOA+UN3lJlLM3NhZi6cNXdWh8KVpLGxvq+fKb0OMiVJktRJdSe41wOHRcQhETGNMojU\n5UPW+Q1wLEBEPJOS4D5Ya5SSNMbueWQjB+3hxThJkqROqjXBzcx+4DTgSuB2ymjJt0bEhyLixGq1\ndwFvjYj/Ai4C3pyZWWecktQJPiZIkiSps+oeZIrMvAK4Ysi8D7RM3wa8qO64JEmSJEkT23gcZEqS\nJEmSpB1mgitJkiRJ6gomuJIkSZKkrmCCK0mSJEnqCia4klSD+9dsZN6sqU2HIUmS1NVMcCWpBus2\nbmav2dObDkOSJKmrmeBKkiRJkrqCCa4kddiGvi1s7B9g1rTepkPRJBIRz2o6BkmS6maCK0kd9vD6\nPrYMJPvPm9l0KJpc/jEirouIv4yIuU0HI0lSHUxwJakmEU1HoMkkM38feD2wALgxIr4SEcc1HJYk\nSR1lgitJUpfKzF8C7wfeA/wh8MmIuCMiXtNsZJIkdYYJriR12P1rNwIwd+a0hiPRZBIRz46IjwO3\nAy8BXpmZz6ymP76dbXsj4mcR8c3q9SERcW1ELI+ISyLCg1mSNC6Z4EpSh63d2A/AXnN8TJBq9Sng\np8DvZOZfZeZPATLzXspd3dG8g5IYDzob+HhmPhV4GDi1A/FKkrTLTHAlqcM29w8A9sFV7V4OfCUz\nNwBERE9EzALIzAtH2igiDqy2/Xz1Oih3fS+rVrkAeFUH45YkaaeZ4EpSh/1m9XoADtpjVsORaJL5\nLtA6dPesat72fAL4P8BA9XpP4JHM7K9erwQOGKsgJUkaS7UnuBFxfEQsq/rxLB5hnddFxG0RcWtE\nfKXuGCVpLA1kAjBtitcUVasZmfno4ItqetSrLBHxCuCBzLxxZ94wIhZFxA0RccP6Net3ZheSJO2S\nKXW+WUT0Ap8GjqNcAb4+Ii7PzNta1jkMOBN4UWY+HBF71xmjJI21Kr+V6vZYRBw12Pc2In4X2LCd\nbV4EnBgRJwAzgN2Bc4B5ETGluot7IHDPcBtn5lJgKcD+T9/fI1+SVLu6byc8D1iemXdmZh9wMXDS\nkHXeCnw6Mx8GyMwHao5RksbU3asfY86MKcyeVus1RekM4KsR8cOI+BFwCXDaaBtk5pmZeWBmHgyc\nDFyVma8Hvg+8tlrtTcDXOxe2JEk7r+6zrQOAFS2vVwJHD1nnaQAR8WOgF1iSmd+uJzxJGnub+5Pd\npk2hp8dRplSfzLw+Ip4BPL2atSwzN+/k7t4DXBwRfwv8DPjCWMQoSdJYG4+3E6YAhwHHUJpB/SAi\nnpWZj7SuFBGLgEUAc/eZW3eMktS2dZs22/9WTXkucDClbj0qIsjML7WzYWZeDVxdTd9JaYUlSdK4\nVneCew+woOX1cP14VgLXVleZ74qIX1AS3utbV7Kfj6SJ4r41Gx1BWbWLiAuBpwA3AVuq2Qm0leBK\nkjQR1Z3gXg8cFhGHUBLbk4H/MWSdfwVOAb4YEfMpTZbvrDVKSRpjPgNXDVgIHJ7pMGeSpMmj1jZz\n1eiLpwFXArcDl2bmrRHxoYg4sVrtSuChiLiNMqjFX2fmQ3XGKUlSF7gF2LfpICRJqlPtfXAz8wrg\niiHzPtAyncA7qz9JmvAeeqyPvefMaDoMTT7zgdsi4jpg0+DMzDxx5E0kSZrYxuMgU5LUVVY/1sd+\n80xwVbslTQcgSVLdTHAlqYM2bxmgr3+AwE64qldmXhMRTwYOy8zvRsQsyuP3JEnqWj63QpI66Jpl\nD7Kpf4DnH7pH06FokomItwKXAedWsw6gDOQoSVLXMsGVpA768a9+C8DRh+7ZcCSahP4KeBGwFiAz\nfwns3WhEkiR1mAmuJHXI/Ws38uWf3M0Jz9qXuTOnNh2OJp9Nmdk3+CIiplCegytJUtcywZWkDrlt\n1Vo2b0ne8PwnNx2KJqdrIuK9wMyIOA74KvCNhmOSJKmjTHAlqUNWrl4PwFP2mt1wJJqkFgMPAj8H\n/hflEX3vbzQiSZI6zFGUJalD1vdtAWDGFAeuVf0ycwD4XPUnSdKkYIIrSR2y8uENzJ05lbmz7H+r\n+kXEXQzT5zYzD20gHEmSamGCK0kd1Nvj82/VmIUt0zOAPwF8XpUkqavZB1eSpC6UmQ+1/N2TmZ8A\nXt50XJIkdZJ3cCVJ6kIRcVTLyx7KHV3rfUlSV7Oik6QO6esfwBbKatBHW6b7gV8Dr2smFEmS6mGC\nK0kd8pvV6zloj1lNh6FJKjNf3HQMkiTVzQRXkjokSab0OtSBmhER7xxteWZ+rK5YJEmqS+1nXhFx\nfEQsi4jlEbF4lPX+e0RkRCwcaR1JkjSihcDbgAOqv78AjgLmVH+SJHWdWu/gRkQv8GngOGAlcH1E\nXJ6Ztw1Zbw7wDuDaOuOTJKmLHAgclZnrACJiCfCtzHxDo1FJktRBdd/BfR6wPDPvzMw+4GLgpGHW\n+7/A2cDGOoOTJKmL7AP0tbzuq+ZJktS16u6DewCwouX1SuDo1hWqxxosyMxvRcRf1xmcJEld5EvA\ndRHxL9XrVwEXNBiPJEkdN64GmYqIHuBjwJvbWHcRsAhg7j5zOxuYJEkTTGZ+OCL+Dfj9atZbMvNn\nTcYkSVKn1d1E+R5gQcvrA6t5g+YARwBXR8SvgecDlw830FRmLs3MhZm5cNZcH8MhafxZ/Vgfu03r\nbToMTW6zgLWZeQ6wMiIOaTogSZI6qe4E93rgsIg4JCKmAScDlw8uzMw1mTk/Mw/OzIOBnwAnZuYN\nNccpSbts9WN97DdvZtNhaJKKiL8B3gOcWc2aCny5uYgkSeq8WhPczOwHTgOuBG4HLs3MWyPiQxFx\nYp2xSFIdoukANJm9GjgReAwgM+/FxwNJkrpc7X1wM/MK4Ioh8z4wwrrH1BGTJEldqC8zMyISICJ2\nazogSZI6re4mypIkqR6XRsS5wLyIeCvwXeBzDcckSVJHjatRlCVJ0tjIzH+IiOOAtcDTgQ9k5nca\nDkuSpI4ywZUkqctERC/w3cx8MWBSK0maNGyiLElSl8nMLcBARPigeEnSpOIdXEmSutOjwM8j4jtU\nIykDZObbR9ogIhYAXwL2ARJYmpnnRMQewCXAwcCvgddl5sOdC12SpJ1jgitJUnf65+pvR/QD78rM\nn0bEHODGKkF+M/C9zDwrIhYDiynP2JUkaVwxwZUkqYtExEGZ+ZvMvGBHt83MVcCqanpdRNwOHACc\nBBxTrXYBcDUmuJKkccg+uJLUIf0D2XQImpz+dXAiIr62szuJiIOBI4FrgX2q5BfgPkoTZkmSxh0T\nXEnqgDUbNvPI+s0ctMespkPR5BMt04fu1A4iZgNfA87IzLWtyzIzKf1zh9tuUUTcEBE3rF+zfmfe\nWpKkXWKCK0kdMFDdvZ0+xZ9Z1S5HmG5LREylJLf/lJmDfXjvj4j9quX7AQ8M+8aZSzNzYWYunDXX\nizuSpPp55iVJUnf5nYhYGxHrgGdX02sjYl1ErB1tw4gI4AvA7Zn5sZZFlwNvqqbfBHy9I5FLkrSL\nHGRKkqQukpm9u7D5i4A3Uh4vdFM1773AWcClEXEqcDfwul2LUpKkzjDBlaQOePDRTQDsPnNqw5FI\n7cvMH/HEPrytjq0zFkmSdoZNlCWpAx5+rA+AfXef0XAkkiRJk4cJriRJkiSpK9Se4EbE8RGxLCKW\nR8TiYZa/MyJui4ibI+J7EfHkumOUJEmSJE08tSa4EdELfBp4GXA4cEpEHD5ktZ8BCzPz2cBlwEfq\njFGSJEmSNDHVfQf3ecDyzLwzM/uAi4GTWlfIzO9n5uDT4X8CHFhzjJK0y3b44aOSJEnaZXUnuAcA\nK1per6zmjeRU4N86GpEkdcCK1eU63d4OMiVJklSbcfuYoIh4A7AQ+MMRli8CFgHM3WdujZFJ0vZt\n6h8AYPeZ4/ZnVpIkqevUfQf3HmBBy+sDq3lPEBEvBd4HnJiZm4bbUWYuzcyFmblw1txZHQlWkiRJ\nkjRx1J3gXg8cFhGHRMQ04GTg8tYVIuJI4FxKcvtAzfFJ0pi4f+1GenuCOdOnNh2KJEnSpFFrgpuZ\n/cBpwJXA7cClmXlrRHwoIk6sVvt/wGzgqxFxU0RcPsLuJGncWrexn9nTpzBzWm/ToUiSJE0atXcO\ny8wrgCuGzPtAy/RL645JkiRJkjTx1d1EWZIkSZKkjjDBlaQO2Lh5C7090XQYkiRJk4oJriR1wIqH\n13PQHo7wLkmSVCcTXEnqkCnewZUkSaqVCa4kdUD/lmw6BEmSpEnHBFeSOuA3q9ezwCbKkiRJtTLB\nlaQO2DKQzJjqT6wkSVKdPPuSJEmSJHUFE1xJGmOZyab+AXrCQaYkSZLqZIIrSWNs3aZ+1mzYzJP3\ntA+uJElSnUxwJWmMZTWAcm+PP7GSJEl18uxLksbYwICPCJIkSWqCCa4kjbG7V68H4MAnzWw4EkmS\npMnFBFeSxtiWgQEAZk7tbTgSSZKkyWVK0wFI0kSTmQwk9A8MsGUgH//rr/69bdU6AGZOM8GVJEmq\nU+0JbkQcD5wD9AKfz8yzhiyfDnwJ+F3gIeBPM/PXdccpqXlbBoJH109n7frprF0/g77NvWQGmcFA\nUv0b1Txapsv8v1hxI/0DyUAOJp8D9G9pfZ3bvh4YYGBga/LaP0wCu6XNPraHzN+twyUkSZKkVrUm\nuBHRC3waOA5YCVwfEZdn5m0tq50KPJyZT42Ik4GzgT8dbb9zHlrHkhd/cJv5H/3qO1k3fw7HnH81\nx1xwjctd7vIJtPyl7z6X5b37c8aPvsIZP75om+UvPP08HpyzJ2//4Vc4/YeXDLv9nfkof3bl+bzh\nyvO3WX76P3yDdXvszWu+/jlO/Prnt1n+4aXfYf38fXnpJZ/hxZd8ZpvlX7j4h2zce1+OvuCTLLzg\nU9ss/49r/ov5s6fDkiXwwW1/n7jnHth/f5eP1+WSJGlCisz6RvuMiBcASzLzj6vXZwJk5t+3rHNl\ntc5/RsQU4D5grxwl0P2fvn8uOndRZ4OXVJs7frMXP7ntYBbs/TAH7/Mwc2ZtZPfdNjF9aj8R7e9n\nyTFLOhajultE3JiZC5uOYyKzbpaa8Ynv/uLx6TNe+rQd3r7uuvPgxd8adv6vz3r5Dm3bzvqa2Nqt\nm+tuonwAsKLl9Urg6JHWycz+iFgD7An8tpYIJTVq3fpp3PiLA5k/91FecuTyHUpoJUmSNLlN2EGm\nImIRsAhg7j5zG45G0liZMa2fQ/ZdzbMOvc/kVpIktWXwbq53clX3Y4LuARa0vD6wmjfsOlUT5bmU\nwaaeIDOXZubCzFw4a+6sDoUrqW5TpwzwwiPuZs6sTU2HIkmSpAmm7gT3euCwiDgkIqYBJwOXD1nn\ncuBN1fRrgatG638rSZI6LyKOj4hlEbE8IhY3HY8kScOpNcHNzH7gNOBK4Hbg0sy8NSI+FBEnVqt9\nAdgzIpYD7wSsRCVJalDLUxBeBhwOnBIRhzcblSRJ26q9D25mXgFcMWTeB1qmNwJ/UndckiRpRM8D\nlmfmnQARcTFwEnDbqFtJGhcGR1bemVGVmzLcCMkjjbg83HatfXFH6p/rKMzb6oa+zHU3UZYkSRPP\ncE9BOKChWCRJGlGtz8HtlIhYByxrOo4uMB8fxzRWLMuxYTmODctxxz05M/dqOojxIiJeCxyfmf+z\nev1G4OjMPG3Ieo8/4QA4Aril1kAnLr+j7bOs2mM5tc+yal/TZdVW3TxhHxM0xLJ2Hvqr0UXEDZbj\n2LAsx4blODYsR42Bdp6CQGYuBZaCx92OsKzaZ1m1x3Jqn2XVvolSVjZRliRJ29POUxAkSWpc7AR1\nIwAABOhJREFUt9zBlSRJHZKZ/REx+BSEXuC8zLy14bAkSdpGtyS4S5sOoEtYjmPHshwbluPYsBy1\ny4Z7CsJ2eNy1z7Jqn2XVHsupfZZV+yZEWXXFIFOSJEmSJNkHV5IkSZLUFcZFghsRx0fEsohYHhGL\nh1k+PSIuqZZfGxEHtyw7s5q/LCL+eHv7rAbIuLaaf0k1WEbXqLksz4+IuyLipurvOZ3+fHXpUDme\nFxEPRMQtQ/a1R0R8JyJ+Wf37pE5+tjrVXI5LIuKeluPxhE5+tjqNdTlGxIKI+H5E3BYRt0bEO1rW\n79rjUTvGurl91r3ts35tn3Voe6wj21dzWTV3TGVmo3+UwSp+BRwKTAP+Czh8yDp/CXy2mj4ZuKSa\nPrxafzpwSLWf3tH2CVwKnFxNfxZ4W9NlMIHL8nzgtU1/7olQjtWyPwCOAm4Zsq+PAIur6cXA2U2X\nwQQtxyXAu5v+3BOhHIH9gKOqdeYAv2j5Xnfl8ejfuDjuurJubqCszmeC1r0N1AsT9vesgbJawgSs\nQzv0/evKOrKBsmrsmBoPd3CfByzPzDszsw+4GDhpyDonARdU05cBx0ZEVPMvzsxNmXkXsLza37D7\nrLZ5SbUPqn2+qoOfrW61lWUNn6VJnShHMvMHwOph3q91X910TNZdjt1qzMsxM1dl5k8BMnMdcDtw\nwDD76qbjUTvGurl91r3ts35tn3Voe6wj21d3WTVmPCS4BwArWl6vZNuCeXydzOwH1gB7jrLtSPP3\nBB6p9jHSe01kdZbloA9HxM0R8fGImD4WH2Ic6EQ5jmafzFxVTd8H7LNzYY87dZcjwGnV8XjeRGs2\nNIqOlmPV/OhI4NpqVrcej9ox1s3ts+5tn/Vr+6xD22Md2b66ywoaOqbGQ4KrietM4BnAc4E9gPc0\nG87El6VNh0Ob75zPAE8BngOsAj7abDjjX0TMBr4GnJGZa4cu93iUxiXr3p3g79l2WYcOYR3ZvhHK\nqrFjajwkuPcAC1peH1jNG3adiJgCzAUeGmXbkeY/BMyr9jHSe01kdZYlVbOEzMxNwBepmr90gU6U\n42juj4j9qn3tBzyw05GPL7WWY2ben5lbMnMA+Bwej6OWY0RMpVRG/5SZ/9yyTrcej9ox1s3ts+5t\nn/Vr+6xD22Md2b5ay6rJY2o8JLjXA4dFGUFxGqVD8+VD1rkceFM1/VrgquqqyeXAydWIX4cAhwHX\njbTPapvvV/ug2ufXO/jZ6lZbWcLjX2yqtvmvAp4wIt8E1olyHE3rvrrpmKy1HAePx8qr8XgcsRyr\n7+wXgNsz82Oj7KubjkftGOvm9ln3ts/6tX3Woe2xjmxfrWXV6DGV42NUrxMoo279CnhfNe9DwInV\n9Azgq5QOzdcBh7Zs+75qu2XAy0bbZzX/0Gofy6t9Tm/680/gsrwK+DnlgP0yMLvpzz/Oy/EiShON\nzZS+C6dW8/cEvgf8EvgusEfTn3+CluOF1fF4M+WHeL+mP/94LUfg9yjNqm4Gbqr+Tuj249G/Zo+7\nkfZZzZ/QdXPNZTWh696a64UJ/XtWc1lN2Dp0rMuJLq4jay6rxo6pqAKQJEmSJGlCGw9NlCVJkiRJ\n2mUmuJIkSZKkrmCCK0mSJEnqCia4kiRJkqSuYIIrSZIkSeoKJriSJEmSpK5ggitJkiRJ6gomuJIk\nSZKkrvD/ARlb5s5HN11SAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c80bc3a90>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Veriation of collected metrics with respect to the base kernel"
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "variations_df = plotDeltas(results_df,\n                           ('rt_wakeup', 'dft_rl5000', 'base'),\n                           threshold_pct=25)\nvariations_df",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:45:30,302 INFO    : Deltas wrt: ('rt_wakeup', 'dft_rl5000', 'base')\n2017-06-23 14:45:30,574 INFO    : Limiting x axis to 25[%] variations\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 79,
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">rt_wakeup</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">dft_rl5000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>delta_full_stack</th>\n      <th>delta_ignore_sugov</th>\n      <th>delta_rt_mode</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>freq_avg_cpu2</th>\n      <td>-5.134366</td>\n      <td>-8.167763</td>\n      <td>-9.160908</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_99p</th>\n      <td>1.237534</td>\n      <td>-0.902002</td>\n      <td>1.340576</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_avg</th>\n      <td>1.699376</td>\n      <td>-1.556509</td>\n      <td>-0.409456</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_max</th>\n      <td>-0.450676</td>\n      <td>-3.605408</td>\n      <td>23.485228</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_min</th>\n      <td>-80.263158</td>\n      <td>-81.578947</td>\n      <td>1864.473684</td>\n    </tr>\n    <tr>\n      <th>runtime_rta_rt_std</th>\n      <td>36.333074</td>\n      <td>31.846606</td>\n      <td>-46.075707</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                          rt_wakeup                                 \n                         dft_rl5000                                 \n                   delta_full_stack delta_ignore_sugov delta_rt_mode\nfreq_avg_cpu2             -5.134366          -8.167763     -9.160908\nruntime_rta_rt_99p         1.237534          -0.902002      1.340576\nruntime_rta_rt_avg         1.699376          -1.556509     -0.409456\nruntime_rta_rt_max        -0.450676          -3.605408     23.485228\nruntime_rta_rt_min       -80.263158         -81.578947   1864.473684\nruntime_rta_rt_std        36.333074          31.846606    -46.075707"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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VG5I8EzgEuKp9pECSJEmSpEmbytnyb0uyWzux3hE9lL8H2GISx7se2CbJfgBJNk6yxzj2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\nkiRJmg2n5UuSJEmS1HEm95IkSZIkdZzJvSRJkiRJHWdyL0mSJElSx5n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6AAAA\ncElEQVTPAZ8GfgK4H/jNqhq86Z40byR5IXAdsBrY0Kr/mN51987lecDkXpIkSZKkjnNZviRJkiRJ\nHWdyL0mSJElSx5ncS5IkSZLUcSb3kiRJkiR1nMm9JEmSJEkdZ3IvSZIkSVLHmdxLkiRJktRx/w2H\nusfpgHwTDQAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8142d950>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Energy comparison among the different tested configurations"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "nrg_deltas_df = plotEnergy(energy_df, threshold_pct=15, tag=PERF_TAG, branch=PERF_BRANCH, test=PERF_TEST)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2017-06-23 14:27:38,651 INFO    : Limiting x axis to 15[%] variations\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BfwOTJb2yvN8buBL4XWnfvLR/qGGfdrkDeBVw0zDEFREREREREWNYrYsxxWW0n7IzB5gr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oK8fwHOAibUBZY/Avyhlt2Asg7PDZS1c75m+++UZNbRNdYbgM2hJGaAdYFrR9D+iIiIiIiIGEWy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\negxHAksAN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            "text/plain": "<matplotlib.figure.Figure at 0x7f7c8563bc90>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "nrg_deltas_df",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>energy</th>\n      <th>max_delta</th>\n    </tr>\n    <tr>\n      <th>test</th>\n      <th>tag</th>\n      <th>branch</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fair_wakeup</th>\n      <th>dft_rl500</th>\n      <th>base</th>\n      <td>2.066435</td>\n      <td>379.514692</td>\n    </tr>\n    <tr>\n      <th>fair_small</th>\n      <th>dft_rl500</th>\n      <th>base</th>\n      <td>0.921285</td>\n      <td>113.783493</td>\n    </tr>\n    <tr>\n      <th>fair_wakeup</th>\n      <th>dft_rl500</th>\n      <th>ignore_sugov</th>\n      <td>1.964999</td>\n      <td>355.976544</td>\n    </tr>\n    <tr>\n      <th>fair_small</th>\n      <th>dft_rl500</th>\n      <th>ignore_sugov</th>\n      <td>0.430943</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>fair_wakeup</th>\n      <th>dft_rl500</th>\n      <th>full_stack</th>\n      <td>2.029049</td>\n      <td>370.839299</td>\n    </tr>\n    <tr>\n      <th>fair_small</th>\n      <th>dft_rl500</th>\n      <th>full_stack</th>\n      <td>0.697692</td>\n      <td>61.898905</td>\n    </tr>\n    <tr>\n      <th>fair_wakeup</th>\n      <th>dft_rl500</th>\n      <th>rt_mode</th>\n      <td>1.863417</td>\n      <td>332.404518</td>\n    </tr>\n    <tr>\n      <th>fair_small</th>\n      <th>dft_rl500</th>\n      <th>rt_mode</th>\n      <td>0.435416</td>\n      <td>1.037956</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                                      energy   max_delta\ntest        tag       branch                            \nfair_wakeup dft_rl500 base          2.066435  379.514692\nfair_small  dft_rl500 base          0.921285  113.783493\nfair_wakeup dft_rl500 ignore_sugov  1.964999  355.976544\nfair_small  dft_rl500 ignore_sugov  0.430943    0.000000\nfair_wakeup dft_rl500 full_stack    2.029049  370.839299\nfair_small  dft_rl500 full_stack    0.697692   61.898905\nfair_wakeup dft_rl500 rt_mode       1.863417  332.404518\nfair_small  dft_rl500 rt_mode       0.435416    1.037956"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<center>\n<font color=\"red\" size=\"30\">______ END of REPORT ______</font><br>\n<font color=\"red\" size=\"3\">The following cells report the code used to generate the previous results</font>\n</center>"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT - END OF REPORT",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Code used to generate Previous Results"
    },
    {
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        }
      },
      "cell_type": "markdown",
      "source": "## RTApp compare\n\nThis notebook takes the test results obtained from running automatic RTApp tests using the LISA benchmark infrastructure and compares them.\nThe user has to provide the paths to the folders containing the RTApp results and the test configurations he wants compared."
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import logging\nreload(logging)\nlog_fmt = '%(asctime)-9s %(levelname)-8s: %(message)s'\nlogging.basicConfig(format=log_fmt)\n\n# Change to info once the notebook runs ok\nlogging.getLogger().setLevel(logging.INFO)",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%pylab inline\n\nimport pandas as pd\nimport pylab as plt\nimport sqlite3\nimport matplotlib.cm as cm\nimport re\nimport os\nimport json\n\nfrom matplotlib import collections  as mc\nfrom collections import namedtuple\nfrom trace import Trace",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Populating the interactive namespace from numpy and matplotlib\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Configuration"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Provide the root path where your test folders are stored\nresults_dir = '/data/Code/lisa/results/schedutil/flags_v3/rtapp/'\n\ndef _select(in_df, tag='multiple.*', branch='.*', test='.*'):\n    out_df = in_df[in_df.tag.str.match(tag)]\n    out_df = out_df[out_df.branch.str.match(branch)]\n    out_df = out_df[out_df.test.str.match(test)]\n    return out_df\n\n# Setup the dictionary to collect comparison results\nresults = {}",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false
        }
      },
      "cell_type": "markdown",
      "source": "## Performance Analysis"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Data Collection"
    },
    {
      "metadata": {
        "run_control": {
          "marked": false
        },
        "code_folding": [],
        "trusted": true
      },
      "cell_type": "code",
      "source": "def load_prf_db(dirname, basedir=results_dir):\n    branch, tag, test = dirname.replace(basedir, '').split('/')\n    return []\n\ndef load_nrg_db(dirname, basedir=results_dir):\n    branch, tag, test = dirname.replace(basedir, '').split('/')\n    nrg_file = os.path.join(dirname, 'energy.json')\n    \n    energy = 0\n    if os.path.isfile(nrg_file):\n        with open(nrg_file, 'r') as fh:\n            nrg_data = json.load(fh)\n            energy = float(nrg_data['big'])\n\n    return ((branch, tag, test, energy))\n\ndef load_results(arg, dirname, fnames, basedir=results_dir):\n    global prf_data\n    global nrg_data\n    if 'platform.json' not in fnames:\n        return\n\n    branch, tag, test = dirname.replace(basedir, '').split('/')\n    logging.info(\"Loading %-16s: %-32s: %s...\", branch, tag, test)\n    \n    prf_data.extend(load_prf_db(dirname, basedir))\n    nrg_data.append(load_nrg_db(dirname, basedir))\n    \nprf_data = []\nnrg_data = []\nos.path.walk(results_dir, load_results, None)\n\nenergy_df = pd.DataFrame(nrg_data, columns=['branch', 'tag', 'test', 'energy'])\nlogging.info(\"Total energy rows: %d\", len(energy_df))\n# logging.info(\"\")\n# logging.info(\"Loaded table example:\")\n# jankbench_df.head()\n\nlogging.info(\"Loaded configurations:\")\nenergy_df[['branch', 'tag', 'test']]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def availableTests():\n    return energy_df[['branch', 'tag', 'test']]\n\ndef idxToKeys(test_id):\n    # Energy is collected for all tests, let's use its entries\n    # to returns the keys which corresponds to a given index.\n    return tuple(energy_df.ix[test_id][['test', 'tag', 'branch']].values.tolist())",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Analysis selection"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Select the tag and branch for performance analysis\nPERF_TAG = 'dft_rl500$'\nPERF_BRANCH = '.*'\nPERF_TEST = '.*'\n\n# Select the tag and branch for performance analysis\nTRACE_TAG = 'dft_rl500'\nTRACE_BRANCH = '.*'\nTRACE_TEST = '.*'",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Data Processing and Plotting Functions"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "CDF = namedtuple('CDF', ['df', 'threshold', 'above', 'below'])\n\ndef getCDF(data, threshold):\n    \"\"\"\n    Build the \"Cumulative Distribution Function\" (CDF) for the given data\n    \"\"\"\n    # Build the series of sorted values\n    ser = data.sort_values()\n    if len(ser) < 1000:\n        # Append again the last (and largest) value.\n        # This step is important especially for small sample sizes\n        # in order to get an unbiased CDF\n        ser = ser.append(pd.Series(ser.iloc[-1]))\n    df = pd.Series(np.linspace(0., 1., len(ser)), index=ser)\n\n    # Compute percentage of samples above/below the specified threshold\n    below = float(max(df[:threshold]))\n    above = 1 - below\n    return CDF(df, threshold, above, below)\n\ndef plotCDF(df, threshold_ms=16, tag='multiple.*', branch='.*', test='.*'):\n    _df = _select(df, tag, branch, test)\n    if _df.empty:\n        logging.warning(\"No data to plot for (tag: %s, branch: %s)\", tag, branch)\n        return None\n    \n    test_cnt = len(_df.groupby(['test', 'tag', 'branch']))\n    colors = iter(cm.rainbow(np.linspace(0, 1, test_cnt)))\n\n    logging.info(\"Below %d[ms] threshold:\", threshold_ms)\n\n    fig, axes = plt.subplots()\n    axes.axvspan(0, threshold_ms, facecolor='g', alpha=0.1);\n\n    labels = []\n    lines = []\n    for keys, df in _select(df, tag, branch).groupby(['test', 'tag', 'branch']):\n        labels.append(\"{:16s}: {:32s}\".format(keys[2], keys[1]))\n        color = next(colors)\n        cdf = getCDF(df.total_duration, threshold_ms)\n        ax = cdf.df.plot(ax=axes, legend=False, xlim=(0,None), figsize=(16, 6),\n                    title='Total duration CDF ({:.1f}% within {} [ms] threshold)'\\\n                          .format(100. * cdf.below, threshold_ms),\n                    label=test, color=color)\n        lines.append(ax.lines[-1])\n        axes.axhline(y=cdf.below, linewidth=1,\n                     linestyle='--', color=color)\n        logging.info(\"%-16s: %-32s: %.1f\", keys[2], keys[1], 100.*cdf.below)\n\n    axes.grid(True)\n    axes.legend(lines, labels)",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plotEnergy(df, tag='multiple.*', branch='.*', test='.*', threshold_pct=10):\n    global results\n\n    # Compute Energy deltas wrt most energy efficient execution\n    _df = _select(df, tag, branch, test)\n    if _df.empty:\n        logging.warning(\"No data to plot for (tag: %s, branch: %s)\", tag, branch)\n        return None\n    \n    xmin = _df.energy.min()\n    _df['max_delta'] = 100. * (_df.energy - xmin) / xmin\n    \n    # Save energy and deltas in overall results\n    for keys, df in _df.groupby(['test', 'tag', 'branch']):\n        test_df = results.get(keys, {})\n        test_df['energy'] = df.energy.values[0]\n#         test_df['energy_delta'] = df.max_delta.values[0]\n        results[keys] = test_df\n  \n    # Plot absolute and relative energy\n    df = _df.set_index(['test', 'tag', 'branch'])    \n    fig, axes = plt.subplots(2, 1, figsize=(16,8))\n\n    # Plot absolute energy numbers\n    ax=axes[0]\n    df.energy.plot(\n        kind='barh', \n        ax=ax,\n        table=False);\n    ax.grid(True)\n    xmin, xmax = (df.energy.min()*0.99, df.energy.max()*1.01)\n    ax.set_xlim(xmin, xmax)\n    ax.set_ylabel(\"Test Configuration\")\n    ax.set_xlabel(\"Energy (mJ)\");\n\n    # Plot relative energy numbers\n    logging.info(\"Limiting x axis to %d[%%] variations\", threshold_pct)\n    ax=axes[1]\n    df.max_delta.plot(\n        kind='barh', \n        ax=ax,\n        table=False);\n    ax.grid(True)\n    ax.set_xlim(0, threshold_pct)\n    ax.set_ylabel(\"Test Configuration\")\n    ax.set_xlabel(\"Energy Variation [%] wrt minimal consumption configuration\");\n    \n    return df",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Trace Analysis"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Data Collection"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "events = [\n    \"binder_set_priority\",\n    \"sched_switch\",\n    \"sched_wakeup\",\n    \"sched_overutilized\",\n    \"sched_contrib_scale_f\",\n    \"sched_load_avg_cpu\",\n    \"sched_load_avg_task\",\n    \"sched_tune_tasks_update\",\n    \"sched_tune_config\",\n    \"sched_tune_boostgroup_update\",\n    \"sched_boost_cpu\",\n    \"sched_boost_task\",\n    \"sched_energy_diff\",\n    \"sched_energy_perf_deltas\",\n    \"cpu_frequency\",\n    \"cpu_idle\",\n    \"cpu_capacity\",\n]\n\ndef parse_trace(dirname, basedir=results_dir):\n    branch, tag, test = dirname.replace(basedir, '').split('/')\n\n    tracefile = os.path.join(dirname, 'trace.dat')\n    if not os.path.isfile(tracefile):\n        tracefile = os.path.join(dirname, 'systrace.html')\n    if not os.path.isfile(tracefile):\n        raise ValueError('No valid trace found in [%s]', dirname)\n    \n    logging.info(\"Parsing [%s]...\", tracefile.replace(basedir, ''))\n    \n    platfile = os.path.join(dirname, 'platform.json')\n    with open(platfile, 'r') as fh:\n        platform = json.load(fh)\n    \n    trace = Trace(platform, tracefile, events)\n    \n    return ((branch, tag, test, trace))\n\n# Tag and Branch filters for traces to parse\nloadParams = namedtuple('loadParams', ['basedir', 'tag', 'branch', 'test'])\nloadParams.__new__.__defaults__ = (results_dir, 'traced.*', '.*', '.*')\n \ndef load_trace(params, dirname, fnames):\n    global traces\n    \n    if 'platform.json' not in fnames:\n        return\n\n    if 'trace.dat' not in fnames and \\\n       'trace.html' not in fnames:\n        return\n\n    # Skip a folder if it does not match the specified tag and branch filters\n    _branch, _tag, _ = dirname.replace(params.basedir, '').split('/')\n    match = re.search(params.branch, _branch)\n    if not match:\n        return\n    match = re.search(params.tag, _tag)\n    if not match:\n        return\n\n    traces.append(parse_trace(dirname, params.basedir))\n",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "traces = []\nargs = loadParams(results_dir, tag=TRACE_TAG, branch=TRACE_BRANCH, test=TRACE_TEST)\nos.path.walk(results_dir, load_trace, args)\n\ntrace_df = pd.DataFrame(traces, columns=['branch', 'tag', 'test', 'trace'])\nlogging.info(\"Total traces rows: %d\", len(trace_df))\n# logging.info(\"\")\n# logging.info(\"Loaded table example:\")\n# trace_df.head()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Data Processing and Plotting Functions"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def getTrace(df, tag, branch, test):\n    df = _select(df, tag, branch, test)\n    if len(df) > 1:\n        raise ValueError(\"More then one DF matching\")\n    return df.values[-1][-1]",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plotOverutilization(df, tag='traced.*', branch='.*', test='.*'):\n     for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        \n        # Get overutilization data\n        ou_df = trace.data_frame.overutilized()\n        ou_time = ou_df[ou_df.overutilized == 1].len.sum()\n        ou_pct = 100. * float(ou_time) / trace.time_range\n        \n        # Overutilization stats\n        ou_stats_df = pd.DataFrame(ou_df[ou_df.overutilized == 1].len.describe(percentiles=[0.95, 0.99]))\n        ou_stats_df.rename(columns={'len': 'overutilized'}, inplace=True)\n        ou_stats_df = ou_stats_df.T\n        ou_stats_df['freq'] = ou_stats_df['count'].values[0] / trace.time_range\n        \n        # Store results in global dictionary\n        test_df = results.get(keys, {})\n        test_df['ou_count'] = ou_stats_df['count'].values[0]\n        test_df['ou_avg'] = ou_stats_df['mean'].values[0]\n        test_df['ou_min'] = ou_stats_df['min'].values[0]\n        test_df['ou_max'] = ou_stats_df['max'].values[0]\n        test_df['ou_99p'] = ou_stats_df['99%'].values[0]\n        test_df['ou_freq'] = ou_stats_df['freq'].values[0]\n        test_df['ou_time'] = ou_time\n        test_df['ou_pct'] = ou_pct\n        results[keys] = test_df\n        \n        # Log stats\n        logging.info(\"   count: %.0f, pct: %.1f, freq: %.1f, avg: %.6f, 99p: %.6f\",\n                     test_df['ou_count'], test_df['ou_pct'], test_df['ou_freq'],\n                     test_df['ou_avg'], test_df['ou_99p'])\n\n        # Plot the overutilized status\n        trace.analysis.status.plotOverutilized()\n        \n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getTimeRange(trace, task):\n    if task is None:\n        return (0, None)\n    df = trace.data_frame.runtimes_df(task)\n    return (df.index[0], df.index[-1])",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plotFrequencies(df, cpus=None, task=None, tag='traced.*', branch='.*', test='.*'):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        \n        # Limit analysis to task execution interval\n        tmin, tmax = getTimeRange(trace, task)\n        trace.setXTimeRange(tmin, tmax)\n        \n        test_df = results.get(keys, {})\n        if cpus:\n            avgs = trace.analysis.frequency.plotCPUFrequencies(cpus)\n            plt.show()\n            \n            # Store results in global dictionary\n            for cpu, avg in avgs.iteritems():\n                test_df['freq_avg_cpu{}'.format(cpu)] = avg\n                \n        else:\n            lavg, bavg = trace.analysis.frequency.plotClusterFrequencies()\n            plt.show()\n\n            # Store results in global dictionary\n            test_df['freq_avg_little'] = lavg\n            test_df['freq_avg_big'] = bavg\n            \n        results[keys] = test_df\n        \n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def testNotCoscheduled(trace, tmin, tmax):\n    rta_df = trace.data_frame.runtimes_df('rta_fr')[tmin:tmax]\n    rta_fr_cpus = set(rta_df.groupby('cpu').sum().index.tolist())\n    df = trace.data_frame.runtimes_df('sugov:1')[tmin:tmax]\n    rta_sugov_cpus = set(df.groupby('cpu').sum().index.tolist())\n    shared_cpus = rta_fr_cpus.intersection(rta_sugov_cpus)\n    if len(shared_cpus):\n        logging.warning('Workload and Sugov co-scheduled on {}'.format(shared_cpus))\n    return len(shared_cpus) == 0",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plotTaskRuntimes(df, tasks, threshold_ms=2, tag='traced.*', branch='.*', test='.*', check_cosheduling=False):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        \n        # Limit analysis to task execution interval\n        tmin, tmax = getTimeRange(trace, tasks[0])\n        trace.setXTimeRange(tmin, tmax)\n        \n        if check_cosheduling:\n            assert(testNotCoscheduled(trace, tmin, tmax) == True)\n        \n        test_df = results.get(keys, {})\n        \n        # Plot runtimes for each specified task\n        for task in tasks:\n            stats = trace.analysis.latency.plotRuntimes(task, threshold_ms=threshold_ms)\n            # Dump plots before continuing with next conf\n            plt.show()\n    \n            test_df['runtime_{}_avg'.format(task)] = stats.ix['mean','running_time']\n            test_df['runtime_{}_99p'.format(task)] = stats.ix['99%','running_time']\n            test_df['runtime_{}_std'.format(task)] = stats.ix['std','running_time']\n            test_df['runtime_{}_max'.format(task)] = stats.ix['max','running_time']\n            test_df['runtime_{}_min'.format(task)] = stats.ix['min','running_time']\n            \n        results[keys] = test_df",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getClusterActivations(df, tag='traced.*', branch='.*', test='.*'):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        for cls in ['little', 'big']:\n            # Extract trace from dataframe\n            trace = df.values[-1][-1]\n            cpus = trace.platform['clusters'][cls]\n            active_wave = trace.getClusterActiveSignal(cpus)\n\n            # Get overall and active time\n            cls_time = active_wave.index[-1]\n            cls_active = trace.integrate_square_wave(active_wave)\n            cls_active_pct = 100. * cls_active / cls_time\n            \n            logging.info(\"active_time %-8s [%-64s]: %10.6f (%5.1f%%)\",\n                         cls, keys, cls_active, cls_active_pct)\n\n            # Store results in global dictionary\n            test_df = results.get(keys, {})\n            test_df['trace_time_{}'.format(cls)] = cls_time\n            test_df['active_pct_{}'.format(cls)] = cls_active_pct\n            results[keys] = test_df\n            \n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getUiLatencies(df, tasks, tag='traced.*', branch='.*', test='.*'):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        test_df = results.get(keys, {})\n\n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        for (name, tag) in tasks:\n            pids = trace.getTaskByName(name)\n            max_activations = 0\n            for pid in pids:\n                lat_df = trace.data_frame.latency_df(pid)\n                if len(lat_df) > max_activations:\n                    max_activations = len(lat_df)\n                    avg_lat = lat_df.t_delta.mean()\n                    max_lat = lat_df.t_delta.max()\n                    pid_lat = pid\n            lat_df = trace.data_frame.latency_df(pid_lat)\n            test_df['latency_pid_'+tag] = pid_lat\n            stats = lat_df.t_delta.describe(percentiles=[.99])\n            test_df['latency_cnt_'+tag] = stats['count']\n            test_df['latency_avg_'+tag] = stats['mean']\n            test_df['latency_99p_'+tag] = stats['99%']\n            test_df['latency_max_'+tag] = stats['max']\n            logging.info(\"   [%5d:%-20s]: %d\", pid_lat, name, max_activations)\n            logging.info(\"   cnt: %d, avg: %.6f, 99p: %.6f, max: %.6f\",\n                         stats['count'], stats['mean'], stats['99%'], stats['max'])\n\n\n        results[keys] = test_df\n        \n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plotTaskResidency(trace, pid, xlim=(0, None), title=None):\n    # def plotResidency(trace, pid, x_min=0, x_max=None):\n    cpus_count = trace.platform['cpus_count']\n    x_min = max(trace.x_min, xlim[0])\n    x_max = min(trace.x_max, xlim[1]) if xlim[1] else trace.x_max\n    y_min, y_max = (0, cpus_count)\n    \n    # Create figure\n    x_size, y_size = (16, cpus_count/2)\n    fig, axes = plt.subplots(1, 1, figsize=(x_size, y_size))\n    axes.set_xlim(x_min, x_max)\n    axes.set_ylim(y_min, y_max)\n    \n    # Get pixes resultion from image size\n    \n    x_res = x_size * fig.get_dpi()\n    y_res = y_size * fig.get_dpi()\n    \n    # Get X delta which correspond to a pixel width\n    x_step = float(x_max - x_min) / x_res\n    \n    # Get Y delta which correspond to a CPU ID (swimline size)\n    y_step = y_res / (y_max - y_min)\n\n    # Define line width has half of a swimline\n    line_width = y_step / 2.\n    \n    # Get activations intervals\n    act_df = trace.data_frame.latency_df(pid)[x_min-2*x_step:x_max+2*x_step]\n    act_df = act_df[act_df.curr_state == 'A'][['cpu', 't_start', 't_delta']]\n    act_df['t_end'] = act_df.t_start + act_df.t_delta\n\n    # Define activation color based on CPU type\n    def getColor(row):\n        return (1, 0, 0, 1) if row.cpu in [4,5,6,7] else (0, 0, 1, 1)\n    act_df['color'] = act_df.apply(getColor, axis=1)\n    colors = act_df['color'].values.tolist()\n\n    # Generate one line for each activation\n    def getYPos(cpu_id):\n        # Return the line position given the CPU\n        return cpu_id + (1. / y_step) * line_width / 2\n    def getLine(row):\n        # Stretch end point to ensure each activation is visiable with at least 1 pixel at each zoom level\n        x_end = max(row.t_end, row.t_start + x_step)\n        y_pos = getYPos(row.cpu)\n        return [(row.t_start, y_pos), (x_end, y_pos)]\n    act_df['line'] = act_df.apply(getLine, axis=1)\n    lines = act_df['line'].values.tolist()\n    \n    # Plot activations with color\n    lc = mc.LineCollection(lines, colors=colors, offsets=(0,0) , linewidths=line_width)\n    axes.add_collection(lc)\n\n    # Draw swimlines\n    axes.grid(True, axis='y', color='gray', linestyle='-',\n              linewidth=1, which='both')\n    axes.grid(True, axis='x', color='gray', linestyle=':',\n              linewidth=1, which='both')\n    \n    # Set title\n    if title:\n        axes.set_title(title)\n\n    # Add overutilized signal\n    trace.analysis.status.plotOverutilized(axes)  ",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getUiResidencies(df, tasks, tag='traced.*', branch='.*', test='.*'):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n        \n        test_df = results.get(keys, {})\n\n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        \n        # Limit analysis to task execution interval\n        tmin, tmax = getTimeRange(trace, tasks[0][0])\n        trace.setXTimeRange(tmin, tmax)\n        xlim = (tmin, tmax)\n        \n        for (name, tag) in tasks:    \n            try:\n                pid = test_df['latency_pid_{}'.format(tag)]\n            except:\n                raise ValueError(\"This must be called after getUiLatencies() and with the same set (or a subset) of its tasks\")\n            plotTaskResidency(trace, pid, xlim,\n                              title=\"[{}:{}] {}\".format(pid, trace.getTaskByPid(pid), keys))\n            \n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getBoostValues(trace, pid=None):\n    \n    _df = trace.data_frame.trace_event('sched_boost_task')\n    \n    # Compute boost value (if not yet computed)\n    def getBoost(row):\n        return int(round(100. * row['margin'] / (1025 - row['util'])))\n    if 'boost' not in _df.columns:\n        _df['boost'] = _df.apply(getBoost, axis=1)\n\n    if pid:\n        boost_values = _df[_df.pid == pid].boost.unique().tolist()\n        return boost_values\n\n    # If a task is not specified, return the list of boost values used by all tasks\n    return _df.boost.unique().tolist()",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def getUiBoosts(df, tasks, tag='traced.*', branch='.*', test='.*', xlim=(0, None)):\n    for keys, df in _select(df, tag, branch, test).groupby(['test', 'tag', 'branch']):\n        print\n        logging.info(\"%s\", keys)\n\n        test_df = results.get(keys, {})\n\n        # Extract trace from dataframe\n        trace = df.values[-1][-1]\n        trace.setXTimeRange(xlim[0], xlim[1])\n        \n        # Get list of RT tasks, we will skip them since we do not have signals to plot for them\n        rt_df = trace.data_frame.rt_tasks()\n\n        for (name, tag) in tasks:    \n            try:\n                pid = test_df['latency_pid_{}'.format(tag)]\n            except:\n                raise ValueError(\"This must be called after getUiLatencies() and with the same set (or a subset) of its tasks\")\n            # Let's skip RealTime tasks cause we do not (yet) have plotting support\n            if pid in rt_df.index:\n                continue\n            \n            logging.info(\"Boost values: %s\", getBoostValues(trace, pid))\n            trace.analysis.tasks.plotTasks(int(pid),\n                    signals=['util_avg', 'boosted_util', 'sched_overutilized'])\n\n        # Dump plots before continuing with next conf\n        plt.show()",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Overall Results"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "results_df = pd.DataFrame.from_dict(results)",
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def getDeltas(base):\n    \n    df = pd.DataFrame.from_dict(results)\n    \n    # Find ID of the base column\n    num_tests = len(df.columns)\n    for bc in range(num_tests):\n        if df.columns[bc] == base:\n            logging.info(\"Deltas wrt: %s\", base)\n            break\n    else:\n        raise ValueError(\"Base column not found\")\n            \n    # Delta each other colum wrt the base column\n    for tc in range(num_tests):\n        # Skip all tests which are not the same as the base one\n        if df.columns[tc][0] != df.columns[bc][0]:\n            continue\n        # Skip the base results, they will be used to compute deltas\n        if df.columns[tc] == base:\n            continue\n\n        # We can have two clases of results: traced and multiple-run,\n        # do not allow comparisons among different classes of results\n        if 'multiple' in df.columns[tc][1] and \\\n           'multiple' not in base[1]:\n            continue\n        if 'traced' in df.columns[tc][1] and \\\n           'traced' not in base[1]:\n            continue\n\n        label = (df.columns[tc][0], df.columns[tc][1], 'delta_'+df.columns[tc][2])\n        df[label] = 100. * (df.iloc[:,tc] - df.iloc[:,bc]) / df.iloc[:,bc]\n    \n    # Return only delta columns\n    df = df.iloc[:,num_tests:]\n    df = df.dropna(how='all')\n    df = df.dropna(axis=1, how='all')\n    return df",
      "execution_count": 72,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def plotDeltas(df, base, threshold_pct=10):\n    \n    df = getDeltas(base)\n#     return df\n    \n    num_tests = len(df.columns)\n    test_h = 2*(1+len(df)/4)\n    \n    fig, axes = plt.subplots(nrows=num_tests,\n                             figsize=(16, num_tests * test_h))\n\n    logging.info(\"Limiting x axis to %d[%%] variations\", threshold_pct)\n\n    for idx, tc in enumerate(range(num_tests)):\n        try:\n            ax = axes[idx]\n        except:\n            ax = axes\n        df[df.columns[tc]].plot(kind='barh', table=False, ax=ax)\n        ax.set_xlim(-threshold_pct, threshold_pct)\n        ax.grid(True)\n        ax.set_title(df.columns[tc])\n        # Print X axis lable only for the last plot\n        if idx < num_tests-1:\n            ax.set_xticklabels([])\n            ax.set_xlabel('')\n            \n    return df",
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "logging.info(\"Loaded configurations:\")\navailableTests()",
      "execution_count": null,
      "outputs": []
    }
  ],
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