How to extract peaks from digitizer traces.ipynb

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   "id": "b3954c58",
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   "source": [
    "# How to extract digitizer peaks with the SCS Toolbox"
   ]
  },
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    "## Workflow during a beamtime\n",
    "\n",
    "1. Record first data with signal on digitizer.\n",
    "2. Find peak integration parameters using `check_peak_params()`.\n",
    "3. Update the new parameters in DAMNIT, so that for each new run, automatic processing of each run is performed and saved in `usr/processed_runs` folder. As long as the right bunch pattern is selected for peak extraction, there is no need to care about the number of pulses / period, as they will be adjusted to match the bunch pattern of each run.\n",
    "4. For analysis, load digitizer data using `load_processed_peaks()` or `load_all_processed_peaks()`. This is much faster than loading the raw traces and re-performing peak integration.\n",
    "5. Checking the integration parameters used for the processed data can be done via `check_processed_peaks_params()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdcbef95-b471-42a2-b62f-be2646fca9e5",
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   "source": [
    "## Peak-integration parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cupy is not installed in this environment, no access to the GPU\n"
     ]
    }
   ],
   "source": [
    "import toolbox_scs as tb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib widget\n",
    "plt.rcParams['figure.constrained_layout.use'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b464eb",
   "metadata": {},
   "source": [
    "Extracting peaks from a raw trace is done using the `get_digitizer_peaks()` function of the SCS Toolbox, by integration over the area of the peak and subtraction of a baseline. For one peak, the parameters `pulseStart` and `pulseStop` (sample numbers in the raw trace) define the integration region  and `baseStart` and `baseStop` define the baseline region. In most cases, the pulse pattern is regular and there are `npulses` separated by a `period`. The peak extraction is repeated for each peak, with `pulseStart`, `pulseStop`, `baseStart` and `baseStop` shifted by the period.\n",
    "\n",
    "An example of integration parameters:"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f7050acb-d48b-4802-96b8-65ff3f43ed8d",
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   "source": [
    "params = {'pulseStart': 100,\n",
    "          'pulseStop': 120,\n",
    "          'baseStart': 80,\n",
    "          'baseStop': 99,\n",
    "          'period': 96,\n",
    "          'npulses': 25}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad30515c-b30a-458c-a30d-1f65d6b33138",
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   "source": [
    "If the pattern is not regular, a list of starting positions can be provided to `pulseStart`, while `pulseStop`, `baseStart`, `baseStop` remain integers and relate to the first peak only. In such case, `period` does not have a meaning and `npulses` is equal to `len(pulseStart)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cb8ce33-a39f-48b7-bb25-7090bfea4238",
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   "outputs": [],
   "source": [
    "params = {'pulseStart': [100, 200, 500, 600, 900, 1000, 2000, 10000, 15500],\n",
    "          'period': 0,\n",
    "          'pulseStop': 110,\n",
    "          'baseStop': 99,\n",
    "          'baseStart': 90,\n",
    "          'npulses': 9}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc07d359-aee0-412b-8e7a-695ae01947c3",
   "metadata": {},
   "source": [
    "Let's assume that we are interested in the APD signal on diode 8 looking at the FEL, which corresponds to Ch9 of Fast ADC 2 (mnemonic `FastADC2_9raw`). We can check how the peak-finding algorithm performs by using `tb.check_peak_parameters()` and inspecting the found regions of integration. This shows a plot with the first and last pulses identified by the peak-finding algorithm and displays the region of integration and the region for baseline subtraction. The function returns a dictionnary `good_params` that has all parameters necessary to perform the trapezoidal integration over the digitizer trace.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "946601a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pulseStart': 6763, 'period': 96, 'pulseStop': 6770, 'baseStop': 6762, 'baseStart': 6757, 'npulses': 400}\n",
      "Bunch pattern sase3: 400 pulses, 96 samples between two pulses\n",
      "Auto-find peak parameters:  400 pulses, 96 samples between two pulses\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'pulseStart': 6763,\n",
       " 'period': 96,\n",
       " 'pulseStop': 6770,\n",
       " 'baseStop': 6762,\n",
       " 'baseStart': 6757,\n",
       " 'npulses': 400}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
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xwCZOnMjEiRO9nh8yZAiAGrbBYrF4+LSV97IqTkREBCkpKaWOnzhxAoDIyEgA7r33XkaMGKGe9+ZDV12425S3l15lXoRlXe/+jsjISJ+bCNznAb7//ntyc3P59ttviYuLU8/7itfUrFkz5s2bx3XXXceoUaP4+uuvKxxmw21MlFXvL774Ar1ez+LFiz3u+/3333u9p7d2XdlnqiphYWFotVpuv/12HnroIa9lyvMtdP8OTz31FKNGjfJapk2bNh5/e3vmyMhIXn31VV599VWOHDnCjz/+yJNPPsmpU6eqtLlGIpEGluScWbhwoceuv6+++gq73e6xS8xtXB06dIjly5fTtWvXMu+pKIrqRG61Wnnttdfo0qVLuQZWUlISf/zxBwMHDlSPxcTE0LNnT/744w+ysrIICQkBXE7Mv/32G5dddlllH7naEELw22+/0aBBA687Ld3s2bOHDRs2MHr0aK/BRZ9//nl++OEH1bB17wTcsWOHuvsK8Bqny2g0lpqRALjyyiuZPXs2W7dupVu3burxjz/+GEVR6N+/P+Ca0XLPap1v2rRpo8ZGmzRpkvqiPHz4MOvXr69wPf755x/+/vtvLr74YvXYZ599RnBwsPqsI0aM4IUXXiAiIqLMl7y7DsUNSyEE77//vs9rBg8ezK+//srw4cMZMWIEP/zwg+pcXhbZ2dn8+OOPHuFFPvvsMzQajaobiqKg0+nQarVqmfz8fD755JNy738uz1QVAgIC6N+/P9u2baNz587qDJc3Ss5mumnTpg2tWrXi77//5oUXXqiWejVr1ozx48ezcuVK/vjjj2q5p8T/kAaW5Jz59ttv0el0DBo0SN1FePHFF3PjjTcCrg5xyJAhbNu2jVdffRW73e6xhBcVFeWxrPTwww9zxRVXEBERwaFDh3j99dc5duwYv/32m8f3Dhw4kL59+9K5c2dCQkLYuXMnL730Eoqi8Nxzz3mUnTNnDv3792fIkCH83//9H4qiMHfuXM6cOVOqbGXZvHmzGvYgKysLIQTffPMNAJdccok6A3DNNddw8cUX06VLFyIiIjhx4gQfffQRv/32G2+++WaZoRrcs1eTJ09Ww1sUJzs7m5UrV/Lpp5/yyCOPcNVVVxEeHs7dd9/NzJkz0el0fPTRRx4hMdx06tSJL774gi+//JIWLVpgMpno1KkTjz76KB9//DHDhw9n5syZxMXFsWTJEt566y0eeOABWrdufU5yqwoajYbnnnuOcePGcd1113HPPfeQkZGh7gasKLGxsVx99dVMnz6dmJgYPv30U5YvX86LL75IQEAA4JopXLRoEX379uXRRx+lc+fOOJ1Ojhw5wrJly3jsscfo0aMHgwYNwmAwcMsttzB58mQKCgp4++23SU9PL7MOffr0YeXKlQwdOpTBgwfz888/qzslfREREcEDDzzAkSNHaN26NT///DPvv/8+DzzwgLpjcPjw4cybN48xY8Zw7733kpaWxpw5cyo1s1jVZ6oKr732Gn369OHyyy/ngQceID4+nuzsbA4cOMBPP/2k7jZu2bIlZrOZhQsX0q5dO4KCglTj/t1332XYsGEMGTKExMREGjduzNmzZ9mzZw9bt27l66+/LrMOmZmZ9O/fnzFjxtC2bVuCg4PZtGkTS5cu9TkrJpGUSw36f0nqOG4H5i1btoiRI0eKoKAgERwcLG655RZx8uRJtZzbgdnXZ+zYsR73veaaa0RMTIzQ6/UiOjpaJCYmiuTk5FLfP3HiRNG+fXsRHBwsdDqdiI2NFbfddpvPHVG///676NevnwgICBABAQFiwIAB4o8//ij3Od31f/nll72eHzt2rM9nK+6Q++KLL4pLLrlEhIWFCa1WKyIiIsSQIUPE4sWLy/x+q9UqGjZsKLp06eKzjN1uF02aNBGdOnVSj23cuFH06tVLBAYGisaNG4tp06aJ//73v6Wc3JOTk8XgwYNFcHCwADwcxQ8fPizGjBkjIiIihF6vF23atBEvv/yyh0N9WVTWyd2bjAExbdo0j2P//e9/RatWrYTBYBCtW7cWH374oRg7dmyFndyHDx8uvvnmG9GhQwdhMBhEfHy8mDdvXqmyOTk5YsqUKaJNmzbCYDCI0NBQ0alTJ/Hoo496OIv/9NNP4uKLLxYmk0k0btxYPPHEE+KXX34RgFi9erXHc3fo0MHjO3bt2iWio6NFt27dSsmpOO5r16xZIxISEoTRaBQxMTHi6aefLrV77sMPPxRt2rQRRqNRtGjRQsyePVt88MEHpX57tyy8cS7PVNa9AfHQQw95HEtKShJ33XWXaNy4sdDr9SIqKkr06tVLPP/88x7lPv/8c9G2bVuh1+tLtYu///5b3HjjjaJhw4Zq3zFgwADxzjvvqGXcTu4ld4YWFBSI+++/X3Tu3FmEhIQIs9ks2rRpI6ZNm6buKpVIKosiRInIcxJJBZk+fTozZszg9OnTqh+ERFLbiY+Pp2PHjmq6obrCFVdcwZkzZ9To+BKJpHYjdxFKJBKJRCKRVDPSwJJIJBKJRCKpZuQSoUQikUgkEkk1I2ewJBKJRCKRSKoZaWBJJBKJRCKRVDPSwJJIJBKJRCKpZqSBJZFIJBKJRFLNSANLIpFIJBKJpJqRBpZEIpFIJBJJNSMNLIlEIpFIJJJqRhpYEolEIpFIJNWMNLAkVeaKK65g4sSJNV2NCjN9+nS6dOlS09WQSCSSMqlrfavEO9LAkpRJYmIiiqKU+hw4cIBvv/2W55577pzurygK33//ffVUViKR+C2JiYlce+215+Xea9asQVEUMjIyzsv9JfUTXU1XQFL7GTp0KAsWLPA4FhUVhVarLfM6q9WKwWA4n1WTSCQSiaRWImewJOViNBqJjo72+Gi12lLT2PHx8Tz//PMkJiYSGhrKPffcg9VqZfz48cTExGAymYiPj2f27NlqeYDrrrsORVHUv0uSnJyMoih88cUX9OrVC5PJRIcOHVizZo1a5qOPPqJBgwYe133//fcoiuLzudasWcOll15KYGAgDRo0oHfv3hw+fFg9/9NPP9G9e3dMJhMtWrRgxowZ2O32SslOIpHUDubNm0enTp0IDAykadOmPPjgg+Tk5KjnDx8+zMiRIwkLCyMwMJAOHTrw888/k5ycTP/+/QEICwtDURQSExO9foe7H/r+++9p3bo1JpOJQYMGcfToUbWMt5m2iRMncsUVV/is+1tvvUWrVq0wmUw0atSI66+/Xj0nhOCll16iRYsWmM1mLr74Yr755pvKC0hS7cgZLEm18vLLL/Pss88yZcoUAF5//XV+/PFHvvrqK5o1a8bRo0fVzmbTpk00bNiQBQsWMHTo0HJnxJ544gleffVV2rdvz7x587j66qtJSkoiIiKi0vW02+1ce+213HPPPXz++edYrVY2btyoGmS//vort912G6+//jqXX345Bw8e5N577wVg2rRplf4+iURSs2g0Gl5//XXi4+NJSkriwQcfZPLkybz11lsAPPTQQ1itVtauXUtgYCC7d+8mKCiIpk2bsmjRIkaPHs3evXsJCQnBbDb7/J68vDxmzZrF//73PwwGAw8++CA333wzf/zxR5XqvXnzZiZMmMAnn3xCr169OHv2LL///rt6fsqUKXz77be8/fbbtGrVirVr13LbbbcRFRVFv379qvSdkupBGliSclm8eDFBQUHq38OGDePrr7/2WnbAgAE8/vjj6t9HjhyhVatW9OnTB0VRiIuLU89FRUUB0KBBA6Kjo8utx/jx4xk9ejQAb7/9NkuXLuWDDz5g8uTJlX6mrKwsMjMzGTFiBC1btgSgXbt26vlZs2bx5JNPMnbsWABatGjBc889x+TJk6WBJZHUQYrPtjdv3pznnnuOBx54QDWwjhw5wujRo+nUqRPg0nk34eHhADRs2LDUTHlJbDYbb7zxBj169ADgf//7H+3atWPjxo1ceumlla73kSNHCAwMZMSIEQQHBxMXF0fXrl0ByM3NZd68eaxatYqePXuq9V63bh3vvvuuNLBqGGlgScqlf//+vP322+rfgYGBPssmJCR4/J2YmMigQYNo06YNQ4cOZcSIEQwePLhK9XB3IAA6nY6EhAT27NlTpXuFh4eTmJjIkCFDGDRoEAMHDuTGG28kJiYGgC1btrBp0yZmzZqlXuNwOCgoKCAvL4+AgIAqfa9EIqkZVq9ezQsvvMDu3bvJysrCbrdTUFBAbm4ugYGBTJgwgQceeIBly5YxcOBARo8eTefOnSv9Pe6+yU3btm1p0KABe/bsqZKBNWjQIOLi4mjRogVDhw5l6NChXHfddQQEBLB7924KCgoYNGiQxzVWq1U1wiQ1h/TBkpRLYGAgF110kfpxGyG+yhanW7duJCUl8dxzz5Gfn8+NN97o4T9wrriX9DQaDUIIj3M2m63MaxcsWMCGDRvo1asXX375Ja1bt+bPP/8EwOl0MmPGDLZv365+du7cyf79+zGZTNVWf4lEcv45fPgwV111FR07dmTRokVs2bKFN998EyjqJ8aNG8ehQ4e4/fbb2blzJwkJCcyfP79K3+fN97OqfVVwcDBbt27l888/JyYmhqlTp3LxxReTkZGB0+kEYMmSJR591e7du6UfVi1AGliS805ISAg33XQT77//Pl9++SWLFi3i7NmzAOj1ehwOR4Xu4zZ+wOVDtWXLFtq2bQu4lhuzs7PJzc1Vy2zfvr3ce3bt2pWnnnqK9evX07FjRz777DPAZRju3bvXw7B0fzQaqTYSSV1i8+bN2O125s6dy2WXXUbr1q05ceJEqXJNmzbl/vvv59tvv+Wxxx7j/fffB1B3Q1ekr7Lb7WzevFn9e+/evWRkZHj0VSkpKR7XlNdX6XQ6Bg4cyEsvvcSOHTtITk5m1apVtG/fHqPRyJEjR0r1U02bNi23rpLzi1wilJxXXnnlFWJiYujSpQsajYavv/6a6Oho1Y8hPj6elStX0rt3b4xGI2FhYT7v9eabb9KqVSvatWvHK6+8Qnp6OnfddRcAPXr0ICAggKeffpqHH36YjRs38tFHH/m8V1JSEu+99x5XX301sbGx7N27l3379nHHHXcAMHXqVEaMGEHTpk254YYb0Gg07Nixg507d/L8889Xm3wkEkn1kZmZWcpYCQ8Pp2XLltjtdubPn8/IkSP5448/eOeddzzKTZw4kWHDhtG6dWvS09NZtWqV6pcZFxeHoigsXryYq666CrPZ7OGXWhy9Xs/DDz/M66+/jl6vZ/z48Vx22WXq8uCAAQN4+eWX+fjjj+nZsyeffvopu3bt8rmkt3jxYg4dOkTfvn0JCwvj559/xul00qZNG4KDg3n88cd59NFHcTqd9OnTh6ysLNavX09QUJDqQyqpIYREUgZjx44V11xzjddz/fr1E4888oj6d1xcnHjllVc8yrz33nuiS5cuIjAwUISEhIgrr7xSbN26VT3/448/iosuukjodDoRFxfn9XuSkpIEID777DPRo0cPYTAYRLt27cTKlSs9yn333XfioosuEiaTSYwYMUK89957ongTnzZtmrj44ouFEEKkpqaKa6+9VsTExAiDwSDi4uLE1KlThcPhUMsvXbpU9OrVS5jNZhESEiIuvfRS8d5775UvNIlEcsEZO3asAEp9xo4dK4QQYt68eSImJkaYzWYxZMgQ8fHHHwtApKenCyGEGD9+vGjZsqUwGo0iKipK3H777eLMmTPq/WfOnCmio6OFoijqPUuyYMECERoaKhYtWiRatGghDAaDGDBggEhOTvYoN3XqVNGoUSMRGhoqHn30UTF+/HjRr18/9XzxvvX3338X/fr1E2FhYcJsNovOnTuLL7/8Ui3rdDrFa6+9Jtq0aSP0er2IiooSQ4YMEb/99ts5y1RybihClFgMlkhqGcnJyTRv3pxt27bJVDcSiaTW8tFHHzFx4kQZ8V0CSB8siUQikUgkkmpHGlgSiUQikUgk1YxcIpRIJBKJRCKpZuQMlkQikUgkEkk1Iw0siUQikUgkkmpGGlgSiUQikUgk1Uy9CjTqdDo5ceIEwcHBXlMVSCSS2oMQguzsbGJjY/0uOr7sqySSukVV+qt6ZWCdOHFCpgeQSOoYR48epUmTJjVdjQuK7KskkrpJZfqremVgBQcHAy4BhISE1HBtJJUlNzeX2NhYAP73v/+h1+u5e8wYUt0FvvoKQkMhPx+uvdZ17OBBiIysierWKBWWFdRaeWVlZdG0aVNVb/2J2thXSf2rOPVB/ySVoyr9Vb0ysNxT7SEhIbWm05JUHK1Wq/4/NDQUg8GAAqi/ZFiY65OfX3RRcDD44W9dYVlBrZeXPy6R1ca+SupfxalP+iepHJXpr/zL8UEikUgkEonkAiANLIlEIpFIJJJqRhpYEolEIpFIJNWMNLAkEolEIpFIqpl65eQuqduYTCaWLFnC1q1b0ev1GAwGps+ezae7dzOqRQsCDAZXQYMBXn/d5TxqMtVspWuICssKpLwkFULqX8WR+iepCNLAktQatFotl19+OVarFa1Wi1arpXPXrlibNwe9Htw7d7Ra6NYNsrOLjvkZFZaVq7BXeb295iCbk88y76YuhJr1NfAUktrEuepfntXOI19sp31MCI8Oal1DT3FhqA79+3rzUbYeSefZEe0JMMhXcX1ELhFKJH7IqawCXlz6Lyv/PcXcZXtrujqSesAP20+wfPdJXlu5n7X7Ttd0dWo1WQU2nvhmB59vPMqrK/bXdHUk5wlpYElqDTabjffee4+ff/4Zu92O3W5n8XffkfPqqyhLloDd7ipot8OiRfDTT2Cz1Wyla4gKywq8yuvrLcfU019sPEqe1V7yKyR+xrnq39ebj6r//2Bd0oWu/gXlXPXv5x0p6umPNyRTYHNc6EeQXAAUIYSo6UpUF1lZWYSGhpKZmVlrgvdJKk5ubi5BQUEAfP/99xgMBq6/6ipy3QWWLy8KdHj55a5jp05BVJR6j9V7T9GkgZlWjep3dPAKywq8yuvejzezbPdJ9X6f3H0pl7cqkuOFwJ/1tTY++7non93hpNWUX3C/TYKMOrZPHYROWz/H8Oeqf+M/28riYkbWZ/f0oFdLGeG9NlMVna2frV/il2w5fJY7F2ziurfWk5nvnzNbFSU9zwqAWe/yCdlwMK0mqyOp42Tk21TjKtCgJcdiZ09Kds1WqhZzNtfq8fdfh87WUE0k5xNpYEnqDd8ULnvlWOx88PuhGq5N7cbdwV/VKQaADYekgSWpOumF7SksQE+PFhEA/JUk25Qv3Po3tEM0IGVVX5EGlqReIIRg6S411Sq//nOyjNKS9DzXDN/wzq4OfsexTHIs0g9LUjXS3AZWoIFLm4cDsDFJzsr4IqNQ/4Z2dOnftiMZWOzSD6u+IQ0sSb0gz+pQjQaAvSezScux1GCNai9OpyCjcImwY+NQmoabcTgFm5LlC1FSNdwzWOEBRQbWpuSz1CMX32pDCMHZQv3rHhdGZJARi93J30cza7hmkupGGliSeoF7yt2k19Cm0MH9LzmC9kpWgQ1n4XsvLMBAz8IlnT/2n6nBWknqMsVnsDrGhmLSa0jPs7H3pPTDKkme1YHV7gQgIshAjxYug1T6QdY/pIElqRecLTaC7tnSZTDIDss7blkFm3TotRoGtG0IwC+7UnE65YyDpPK4Z7AiAg0YdBp1R9ySYjvlJC7c+mfUaTDrtfS5yCWrVf9Kt4b6hgwfK6k1GI1GvvrqK/7++281/cSTU6fy6T//MKpVKwL0hdHG9Xp46SXX9mejEUCdcg8LNHBZiwg+Wp9crx23KywrKCWv9PRCYzTQlc7jijYNCTLqOJ6Rz4ZDafS+SG4X90eqS/8Aru3amFX/nuLbrcd58IqLMBvqV8aFc9K/rCL9UxSFge0a8bSyk7+PZXI8I5/GDcw19FSS6kYaWJJag06nY+jQoej1ejX9xKW9emFt397VSel07oLQq5cr9UThsbM5RZ3WZS3CURQ4cCqHU9kFNAyuf/m/KiwrV2EPeZ3NzQdcy4MAJr2Wa7rEsvCvIzzz3U5+eaRvvXshSsrnnPSv2AwywKB2jYgMMnI8I59nvt/JvBu71MQjnTfORf/S81zRstz6FxVs5JL4cDYmnWXWkt28OaYbiqLUxGNJqhm5RCipF7jjOoUHGmgQ4PIDAVi151RNVqtWojokBxYlpJ08tC3RISaS0/J457eDNVU1SR3lbK7nDJbZoOXNMV1RFPh263G5gaIY3vTvyWFt0WsVft6Z6hGAVFK3kQaWpNZgs9lYuHAhq1atUtNPrPjlF7LfeQdWrPBM1fHzz65oyYWpJ1Qn28JRoXv785Kd9bOzqrCsoJS83EFYQ0xFo+xQs54pI9oB8OG6JOwO5wV9HknNcy76l1XgOlc8aXiPFhHclNAUgI/WJ1/QZznfnIv+uXfwhgYUyapbszAe6n8RAC8u/VfuvqwnyCVCSa3BarXywAMPAHDllVei0Wh467XXXOkn1qyBUaNcBW02eOEF1/+ffBIoPSoc3imGl3/dy7oDZ9iYdFbdOl5fqLCsoJS88gvznpkNnuo/rGMMoeZdZObb2HE8k27Nws7/g0hqDeeifwVWV5sKKLG0fENCE77YdJR1+8/gcAq0mvqx9HUu+pdXqH+BJWR1f7+WvL3mIMfS89l/KofW9Tzdlz8gZ7Ak9YKzJQys+MhARndrghDw6JfbySqQqXPc5Pl4GWo1CpfJLeOSKuA22k16zzZ1cZMGBJt0LqP9WEYN1Kz2kV+of+YSsjLptepA8HcZMqVeIA0sSb3A7YPlXiIEmH51e5qGmzmekc+bqw/UVNVqHQU27x08oO4g/OOA7OAlFSfPh9Gg02rUMAS/7Tt9wetVG3EbWCYvG0kub+WS1br9Ulb1AWlgSeoFuZbCaXdjUacVbNIzeUhbAFZKZ3cVdQTtpYN3xy/afDhdNcQkkvJQjXYvbapv6ygA1koDCyia7fM2wOlzkUtWfyWdVYORSuou0sCS1AvcnVZACb+ivq2i0BSGbDiRkV8TVat15JXRwbeMCqRhsBGr3cnWw+kXumqSOogQopj++Tawth/NUJfy/ZmyZNU2OpjIIAN5Vgdbj0j9q+tIA0tSL/Dl1xAaoKdL0waAHEG7KWsGS1EUdZnw5131cwempHqxOQSOwgwAJX2wABo3MNOpcShOAe+ulSFAylqi12gUdUn1+23HL2i9JNWPNLAk9YI8q2tbdJlLFNKvAYB8m0tW3kbQADd0bwLA15uPcTpbJsyWlE1+saVkb0YDwMSBrQD43/pkTmYVXJB61VZUHywfsrrtsjgAvt5yjAOnci5YvSTVjwzTIKk1GI1G/ve//7Fz5041/cSkJ5/k0x07GNWmjWeqjpkzoaBATdVRYHP5K/gysF5dsZ91+89gdzjRaev+uKLCsoJS8iqvg+/ZMoKLmzbg76MZvPzrv7x0/cUX4pEkNUxV9c/dnrQaBb3WexiGAW0b0j0ujC2H05n+4z/Mv6VrndbDc9G/vDJmkAES4sMZ1L4Ry3ef5KWl//LeHQkX4pEk5wFpYElqDTqdjuuuu46QkBA1/USfK67A2qVL6VQdAwaoqSfsDifWwsCYAV6MhoubNCAsQE96no1f/znJ8M4xF/Cpzg8VlpWrsIe8fIVpcKMoCs8Ob8f172zgq83HuKhhEPf2bXkBnkpSk1RV//LzCtuTXuszxYuiKDw1rC03vfcnv+xKZcZPu3nu2o4X6Mmqn3PRv7KWCN1MHtKGlXtOsmz3SX7YfpxrujQ+z08kOR/U3SGERFKIxxKFF6NBq1G4o2c8APOW78Vi9+/dcRXp4BPiw5k0qDUAs3/5lxW7T5Z7XyFEnYlAbbFY6NKlC4qisH37do9zR44cYeTIkQQGBhIZGcmECROwWj2ds3fu3Em/fv0wm800btyYmTNnVunZ/zqURnqutc7IzRtlhR0oTkJ8OG+O6QrAJ38e5unvdnLgVLbqv1WcHIud9QfOsPNYJtn1LIZdWbsI3bRqFMz4wsjuT327k/0nsy9I3STVS7XOYFksFnr06MHff//Ntm3b6NKli3ruyJEjPPTQQ6xatQqz2cyYMWOYM2cOBkNR3KKdO3cyfvx4Nm7cSHh4OPfddx/PPvusTHzpJ9jtdr777jt27txJ//79cTgcrFuzhjY7dkCbNnDNNe6CsGqVa8q9Vy/yFdc4QVHAqPM+Zrj78uYs/OswB0/nMvvnf5k2sn2dblcVlpWrsKe8fASFLMmEK1uRkpnP5xuPMu7jzVzRJoreLSMJNLpG4UlncjmTYyHX6iDXYmf3iSwMOg0RQQbaNAqmTXQwFzUMIsCgxaTT0jwqkEbBJjS1IJr35MmTiY2N5e+///Y47nA4GD58OFFRUaxbt460tDTGjh2LEIL58+cDkJWVxaBBg+jfvz+bNm1i3759JCYmEhgYyGOPPVapetz9v81ojLsxaDW0jw0hItDARQ2D6BYXRofYEBo3MF+wdlpl/auAweBmaMcYnhrWltm//Mtnfx3hs7+OYNZriYsIICrYtdx/MquAQ6dzsRczvKKCjTSPDKRlVCA9mkfQIiqQ1o2Cy23D54tq0b9yDNJHBrZmU3I6Gw6lMfrt9TwysDWXt4rEpNMSHepKYJ9vc/DP8Uy2Hc0gLceKQafBqNMQaNTSPiaU3hdF1Ol+rq6jiGocOj3yyCPs37+fX375xcPAcjgcdOnShaioKObOnat2WqNGjfLotFq3bk3//v155pln1E5r2rRpFe60srKyCA0NJTMzk5CQkOp6LMkFIjc3l6CgIAC+//57DAYD1191lSv9BLhyeYWFQX4+XH6569ipUxzWBNDv5TUEGLTsnjnU5/2X7krh/k+3AvD0VW3r9LJXhWUFpeTV6c2tZBfYWfVYP1pEBZX5PRa7g+k//sPnG49WS73Nei3xkYG0iAwkxix4dnTCBdfXX375hUmTJrFo0SI6dOjg0Vf98ssvjBgxgqNHjxIbGwvAF198QWJiIqdOnSIkJIS3336bp556ipMnT2Is9AH8z3/+w/z58zl27FiFXmjuviph6g+ctvh+0UYGGWkXE0xsqJkeLcJpGh5AdIiJhiFGjLrqNS6qqn9/ZCrc+t+/aNMomF8f7Vuh7/rjwBneWHWAbUfTVf/JkjRuYMZid3Imx/tGC5NeQ+MGZgw6LU3DzFwSH05sAzNhgXqy8u2YDVpaRAbSuIG52o36c9G/hHf/5kyOhaUTL6dtdNnt/kyOhXs+3sy2IxlVqmeAQUt8RCDNIwOJiwgg2KTn4qahdGsWhl6r4Vh6HuGBBgINOlKzCmgUYqo36Yyqm6rYF9U2g/XLL7+wbNkyFi1axC+//OJxbtmyZezevduj05o7dy6JiYnMmjWLkJAQFi5cSEFBAR999BFGo5GOHTuyb98+5s2bx6RJk6QVLvFJWXFlijO0YwxThrfj+SV7eGnpXv5NzebyVpGk59ow6bV0iA3hREY+B07lcPhsHiezCsgqsKPTKHRr1oAWUUF0axZGoxAjOq2GIGP56iOEIOlMLhn5Nto0CkZRwKTT1ugsTllBIUti1GmZPaoz9/ZtyXfbjnM4LZdciwNFgdaNgmgYbCLIqCPAoKVxmBmDTsPJLAv7T2bz97FMTmYVkG91kGOxc/RsHvk2B3tSstiTkoXTkne+H7UUJ0+e5J577uH7778nICCg1PkNGzbQsWNHtZ8CGDJkCBaLhS1bttC/f382bNhAv379VOPKXeapp54iOTmZ5s2bV7g+Kx+7AoM5kBMZ+exOySIjz8aGQ2kkn8llb2o2Z3Is/L7fZWB8udnTyA0PNNAoxESjECPRISYCjToCDVpCzHpsDkHbmGBaNQwiyKgj1KxHURSEENXel5bntO2N3hdF0vuiSBxOQdKZHE5kFHA0PQ+DVkN0qInmkYE0CXP9PlkFNpLP5JJ0JpedxzL5+1gGh07nkpZr5eBpl0mzJyWLZT6WsXUahfBAA1qNQmSQkUYhRqKCTTQMNuIUgp3HM8m12DHqtMRHBtA0LACL3YkCNAwx0q1ZGM0jA6vNKb8iS/RuIoOMfHVfT77afJSFfx7hRGY+BTaHh1EaG2qiW1wYTcICsDmcFNgcpOdZWf3vafKsDnanZLE7JcvjvgatBpNeQ1aBHa1GIdCgJavAjkmvQQjXakDPFhF0bBxKgwADWgWMei0xoSaahgfQMNhIZr4No05LiFlX7cZ+faFaDKza1mlJ/Iu8cnbFFefuPs3ZeTyTH7af4Nutx/l2a8VizWzxEnSzU+NQIoIMaBQFh1OQY7GTa7Gj0yqEBRg4np5PalaBWj83Jr0GBQWNAk3DA2gQoKdFVBDtooNpHGYmJtRMbAMzISZdtb8MbQ4nNodr0jpAX3H1bx4ZqPpklUfbaOhXGBqj5HcfS88n6UwOh07nsufISeZVuAbnjhCCxMRE7r//fhISEkhOTi5VJjU1lUaNGnkcCwsLw2AwkJqaqpaJj4/3KOO+JjU11WtfZbFYsFiKZmKysopeeCa9lhZRQepsonubfr7Vwb+pWew7mc3B07lsP5JBalYBqVkFWO1OzuZaOZtrZU8FwpW5jayUzHyahAVg0mvQKAo6rYJWUVyzFkLQ8Prp2DJSWZSkISZIYL7sBn5OP8FVe//wee/KLBGWRKtRuKhhMBc19J3YOMSkp3OTBnRu0kB19nYPXNyy2JOSzY5jGZzJsZCWayXYqCO/cBnb5hCcKgw3kpJZwM4yVH6dj4xaOo1C4zAzjUJMWGwOooMNhFx2A47cDP5JVwgPEOgat2WLgK4n9vp0bhZCFIWUqaC89FoNt/aI49YernbhdAqyCmxoNAo6jVIquLIbq93J0fQ81Tg9ejaPM7lWNiWd5VS2Rd0Y5HAKsgpcdSpuuK3ee5rVeysW2iYyyEB8RCAaRcFid2DUawk164ktNMiGdoxWDWZ/4pwNrNraaUn8h4JydsUVR1EUXr2pC7dfFse3247zx4EzNI8MJC3HyqnsAqJDzbSMCqR5RKDLyDHrybXYWbY7lZNZFnYcy1ANlJ3HMytUP4PONdvljmJdvBP7N9XlvPrnobOlrgs0aIltYCamgZnGDUxEBRlVg63AYiNq9FTsmaf4JkmDonESeOW9vJmbztiti/G18JdfLP2GyXBh97jotRqaR7qWKwa0haysyGoxsKZPn86MGTPKLLNp0ybWr19PVlYWTz31VJllvRm1JWd+SpZxe1r4Mohnz55dbh1LYjZo6dosjK7Nwkp9V0aejZPZBaRmFnAyq4DUTAt5Vjs5FjsZ+S6n8H9Tsjicloe90PjPsbheokfO+p45NLdMwAysTQUQBPUby3ing81v3E64j2sKqjCDda4oiuJhlF7RpqHXcnaHk1PZFtJyrDiF4HS2hZPZBZzKsnAq2xWPq11MCFFBRnIsdpLTcjmeno/ZoMPhdHL0bD5bjqRjtTs5nJbH4TSX7P4GwvqNBeC9fwEEYbfNYTTwxg//YYSPelsdArdrWXk+WL7QaBQaFMu56guDTkPLqCBalnADEEJw5GweKZkFdG4SSmqmq09pERXIqSwLWo1CVoGN3/ef4cjZPHIK7DiEIN/q4Hh6PkfT88izOtBrFexOgRBwJsfKmRzfUfqfX7KHsAA9cYXLldGhJnQahYbBRnKtDvafzCHPaifQqCPf6iAj38rZXBtajcsQbRhiol10MG2jQ2gbE1wl30SHU6BRfOvo+cCngVVfOy1J/cNXollfKIpCQnw4CfG+Xhmlubara+TsmgFykl1gZ93+MwjA4XSiURSCTToCjTpsDidpOVaXcRRqolGIiQCDlsx8G3qthjM5FjSKgtXh5MjZPDLzbOxJzeLgqVxOZOSTkplPep7N1fGcymG/j2CDARddCsDvqYV/J1zNy0CD/Gxu9fEc+YXGnUZxLRPUB8aPH8/NN99cZpn4+Hief/55/vzzT49ZcoCEhARuvfVW/ve//xEdHc1ff/3lcT49PR2bzaYO+KKjo9WBoZtTp1y5LksOJN089dRTTJo0Sf07KyuLpk2bVuwBS6AoCmGBBsICDeX68IBrSepYej7peVbCAvSk5VhxOAUOIbA7BU6nKxJ7Xn4Bd9z3MNrgCK67bjQn8jXsOevAqdGSYzD7NLDOZQbrfKPTaoht4JoRripOpyA1q4AjZ/NIzSwgwKBl19GzvDD/PbQBYbTq3J1sm0JGbgGK3sTRUO9tAKCg2ACnpuSlKApxEYHERQQCePhhxkcWmQQdYkO9Xu+ahXMQYNCSb3NgsTk5fDaPExn5KLgMuwKbk7O5FlIyC9icnM7G5LOk59lIz8tg+9GMKtV7yY6iqdpgo462McG0bhSMTuMy9HQaBUVRKLA5sDsFaTkWth/NQKO4zmfm2zBoNcQ0MBETaiK2cKUgpoGJ5hGBXNo8vNpjs/k0sPyt05LUXfIr4VN0rui1GvRaDQEGHaMLI55XFPeoM7CY75Z7dHktnnFu8qx2UjILXAZXRgHHM/I5mVVAwxAT4QF6hMPOhAkT0DVoxA2jR6PXafhhUxK6hs3JMgX6rIPbwAowVP/yY00RGRlJZGRkueVef/11nn/+efXvEydOMGTIEL788kt69OgBQM+ePZk1axYpKSnExLjipS1btgyj0Uj37t3VMk8//TRWq1XdBb1s2TJiY2NLzcK7MRqNpfrIC4VJr+WihkUv0Yu8T/aQm5tL9tbFAFzz6HUYDDru+zUHxRSEQ+Nbt6rig1WX0GiUUkZa7/hgHhv2GgDv3eZyck98/TfMFw/Bqfh+Sbv1T6dR0NfRAY6iKGofFmDQEWCAsECDmpLMG3lWO8ln8khOyyU5LZfT2RZsDicpGQWEmPU0DQ8gItBArtWOVlFoFGIi1KwvvNbB0fQ8/k3J4t/UbA6eziHbYmdTcjqbkiuXr9Hq8JyJLE6wUUdUiJGLmzSgcQMz6XlW17JwsMu/UWOrfC5bnwZWXe603lt7kL4d4ggx69BpFLQaDWa9lkYhRvWlIoTgTI6VY+muqdIci518q4Nca+G/Fgf5NgcNAvQ0DQtAo7gULTLIwJlsK3qdQkSgkawCG5n5NiICjZzIyCfAoCXQqONkVgGKoqBVXB2c1eFEp9GQEB9GbAMzgQbfQfkqgsXuIC3H5YNhdwqaRwZi1Lle/lqNgs3hVC36kgjhWjI4nJbHvpPZOAXkWuwEGLR0ahJKgF6HXqcQZNQRbNJ7+XbvCCE4mWVBr1U4nWMhJaMAjUYh1KynUYiRQKOOPIuDRiGu38zrjKVGhy44ghO5EKkINEHh2PIy0Tt9x66qzSPoqhJg0Hmd3neTm5vLXX8vBWB44cvwmx/3oWvYvMyXoXuJsKa2t9ckzZo18/jbvQusZcuWNGniMpYHDx5M+/btuf3223n55Zc5e/Ysjz/+OPfcc4+6c2jMmDHMmDGDxMREnn76afbv388LL7zA1KlT643RqiIK/XQ0ZRkN9U//qkRhH2WvgP75m6wCDDrax4bQPvbcdwtb7U4Oncnh35Rs/k3NRgiB2aDF5nBidwiMOg1GvZaIQAPNIgIw613v5PBAA/lWBymZBaRk5nO8cPCakpnP1iOuROTZp+0cOp3r9Xu1jsqneDpnH6za2Gm9vvIAb6w7Uep4dIiJBgF6CmwOUrMKfG4PvhCEmHTotRoELgfUIKOOIJOO4MJ/A42u/5sNWk5mFXAmx4rTKUjJdDm5lpWVXqtxOV03DDbSNDwAvVbBaneSkW8jM89GRr7Na3A/b8SEmmgXE0LLqEBsjkJfjgKXP0e2xY7D6eTwmTwK7A6cggrd1+2AGh3qGqUcOZuH1e6Kxh73xPcAvLgDQBDx0Mf0KEhnlXUDDYqn6nj6aVdcGYOBfKtLIXw5e9ZHDAYDb7/9Nrt370an06HX6+nQoT2HgIJLe7hk5KaYvPIL3W/NF9j/qq6g1WpZsmQJDz74IL179/aI2ecmNDSU5cuX89BDD5GQkEBYWBiTJk3ymE2vi3hrU2aTEQuQc/OYojZVQv8K1Lhq/tOmvMmqU6eOHKCi+udfBlZ1YtBpXL5YFVge90bT8NLO9naHk4Onc0nJzOePA2c4nW2haXgAyWl5ZObbyCmwISz5HKrkd12QN9KF7rQGtmvI3rMOLHbXWqzD6Vozdu/AcaMoLqMrJtREiFlPoMFl0AQatJgNOkx6Dem5Vo6m52Mr3HGRlmMlMthIQeHWc4NOQ7BJR47FTqNgE7lWO3lWB7ENzCiA3ena7RNUaFRsTk7HYnequzaAMo2lstBrXc6OTqcgrdg93EbOqWyLunvGGwEGLR1jQzEZtAQZtZzKsnDgdI7L2LE7sRcadCmZBaz6t3J1axCgJybUNaWemWflZLZFrVd2obPtsfR8jqWXnnbVKQKjTiG3MIDzWVMYZ3pcSYPiqTquusqVekKvr3DgzPqEXq/n1ltvZeXKleh0OnQ6Hc3imnHouMDRomXpVB2F8rIUdvByW7XLxcFbGMBmzZqxePHiMq/t1KkTa9euPV9VqxG8tSmz0YjFCvYel3mmyimmfxapfy79i4/jwDGBPb65T/3LF4X650fGaF1Ap9XQJtoVHNnXhomsrCxCK2mOVLuBVRs6rVdv7loqEFi+1cGWw+lYHQ6CjK4lq5hQV9yeC4nV7qTA7uBIWp66Bu/e4eOaGbKRXThDlGuxk2NxEBVkICrEhFZRiAk1ER3qMgqLx7ax2F3O127DSAEOnHKtVVvtTvRaDQ0C9K6P2UCDAH25HWJWgY29qdnsKdyNZNJrCDLq1Zk29zp8kzAzDQL0KCiEBbr+LSlXi92B0wkCQfKZPIJNOpLTcsm12GkZFYTZoMWg1WCz5PPnut8ICjCD1sCE1XlYHOAoY2KsyAfEvzstd55dexmyshQuUdQXB3fJ+cUdrq0s/XNv9/f3NqWthKzkAMc/8Js1FbNBS59W5fuUnW8MOg0GnYaOjb3v0KgKiqJg0mtLGUwNQ0zndN8Qk55L4sO5pBK77XxRvENxr8OXnKq12+189+sK/v77b3r16oVR50Q47IAOx46d0DjCXRDWr3dFSO7Vq1KB++oLdrudpUuXqrJyOBycSjkBxOBISQV7g+KFVXlZr2oDcMEHFpLaj7c2ZbMUACYcu/dAXLi7oIf+WQpdLfypTXmT1ckTx4FYHKknfetfdDtAGqP+gt8YWJLaj8Vi4cYbbwRc6ScURaEgLxdNQCj6/34AA3u7CtpsMHmy6/8PPFDMB8R/DCxvsvpr3ToCet6AsmkzDCyWBqiYvKwDXNf4ytko8V+8tan0tDPoIpqgW/gZDHSFBSmpfxaH/xlY3mS14fe1BPa6GWXzFhjYoqhwMXlZBrl25vuTrPwZ+StLajdOV+dd1s4cq9097e7fzVkIl6HpKGObuLVw/UJ28JIKUZE2ZZfLXoDaV5W149Lqh8aoPyN/ZUmtRjUaKmBg+X2nVYFt4hZpjEoqQ0WMBql/Liqgf+4BjtQ//0D+ypLajbP8EbRFOtm6cFbAGFU7eD+fbZBUCFERo0EaWECRrCoyGJQGln8gf2VJ7aYiszKFTrZGP/LB8kaFXoZyiUJSGdQZrLJmRV3tzu8HOIWz7Xal/AGO1D//QP7KklpNhUaFcgbLRQVehmoH7++yklQMUXGj3e9nZSqkf7Kv8ifkryyp3ahO7mX5gBSOoP2+g3e/DMtYTpXLOZJKIJe9Ko6oiP7JGSy/QoZpkNQaDAYDc+bMYe/evWr6ifDwBmQBecOGe6bqePRRsFjAYPBLo8GbrC7r1ZNdgLXFRaVTdRTKy4IrGqK/vwwlpfHWpmKio0kD8gZc6VP//NEHy5usevXuxU7A2qKlb/0TLv3zJ1n5M9LAktQa9Ho99957r0f6iQYNQsnKBvulPTxTdYwerabq8McRtDdZXdzlYnbtc2KPiSmdqqNQXtZ82cFLvOOtTTVsGEVausDepWu5+udPbcqr/nXtws69ThyNon3rn9U9wPFvf1F/wX80QlInqVD6CT/s4L1RobQmUlaSSuBuU2XlcPfHGWRvVCqtkJ/Lyl+Qv7Kk1uBwOPj999/ZtWsXDocDh8NBQW6u61xyMjgc7oKwdSvs2AEOh1862XqT1fEjhwGwZ2UXycpVWJWXOzGv7OAlJfHWpnKzMl3njh7zrX9+mN/Sm6xOFOqfIzvHp/5ZbXLHpT8hlwgltYaCggKGDx8OFKWfOJx0CEOzTmi//gb6dYTAQLBaYcIE10V33lmUC03rP9Pu3mT1/VdfEjLycTh4CKyNXLICD3lZF6wH5BKFpDTe2tTe3f9gbN0TzeIlcEVb7/rn8L8wKd5k9e0XnxNy9WTEoSSwNvSuf5/8CcgBjr8gf2VJ7aYSsZ2Mej9vzqICaYXkLiZJZShnF6EQwi9nsLxSAf2z2GUkd39C/sqS2k0lton7ewdfmZhhRj+XlaRilBe81lbMOcvfjfYi/ZO5CCUu5K8sqdVUJDq5RcbBclEhWckZLEklKMdod7cnkLMyMhehpCTyV5bUbsrp4J1CYJOdlgs1b6OMui2pJkTZwTPd7QnkDLIayb0C+icHOP6B/JUltRu3X4OPZM9Wh1yicFORSNLSB0tSGUQ56V/c7UmvVdC44xT4KRVZIlQjufvRhhx/RvayklpNeX5FHiNofzcaKuCDZZEjaEllUGdlyp7B8vvZK6iYv6jUP79ChmmQ1Br0ej3PPfcc+/fvR6vVotPpaB4fRwqQf8mlnpGkH3zQlXqi2HS8P3Xy3mQ1aPBgNgG2sPDSkaQL5SWTPUt84a1NtW7TiqNAwcVdvOqftVD//M1g8Kp/Qwr1r0GYT/2zFIbH8jd5+SvSwJLUGgwGA4888ggrV65Er9ej1+uJbx5PSqrA3qmzZy60MWNcqSc0riZs0GlQFP9ZovAmqyuuHMCmbQ7sQcGlc6EVysuyzXXIn2IWSSqGtzbVqnUrjh4T2Nu09ap/Fj81sLzJqv/AK9m0tWz9s+6SAxx/Qv7KklpNeekn1DyEssMqkhW+DU25pCOpDOWlqpJpcopwS8AhytA/u4zZ50/IX1lSa3A4HGzZsoX9+/er6SeyMzIAsJ8+45mqY88e2LsXq9UO+F8H701Wxw4nAWC32kqn6nDLS74QJT7w1qYyzqYBYD+b7l3/bC7987fMAGXpn8NWAf2TAxy/QP7KklpDQUEB/fv3Z/LkydhsNqxWK3/98TsAmlWrXCknwPXvPffAxIlY8vIB/zMYvMlq/pyXARBZ2UWyAk952WWYBol3vLWpNcuXAaBZv8Gr/lnzCwD/Mxi8yer1l13658jO8al/Vql/foX8lSW1GlFObBkZuK8Y6pb68iNJS3lJKoSz7PQvVhm4tojCmGFOHzsuQe4i9Dfkryyp3ZQTW0bGdSpGOZGk7YoGd2YTKS9JhRAVi4Ml2xMVy6Qg5eVXyF9ZUrspN1WHHBG6KTdmmK5oZ5OUl6QilBc80yJnRFXK0z8nCnan3EXoT8hfWVKrKS8XYVEH719Otl5xy8rHcqpFa1D/Lzt4SYUop01Jn6JilJNJQQ5w/A/5K0tqN6Ls/HoycGYxylvOKezgtRoFnZSXpCKUm0lBLnmplKN/Fm2RgSUHhP6B1ApJ7aY8J1vpNFpEeSPowg5eGqOSilJefksZV62I8mbbrcUMLL3Wf4Ii+zMykruk1qDX63nyySdJSkpS00907NiBJMDaoqVnqo477wSrFYtwdez+ZmB5k9WIa65mA+DUaHFqtUWjp0J5WZxGwP9kJakY3tpUt25d2Q9Ymzbzrn9I/XPL6uprrmE9IBSNV/2zOvwz64Q/Iw0sSa3BYDDw9NNPe6Sf6JrQnaSDTuwtW3qm6rj7blfqicJuzN98QLzJ6oZbbmbDusIlHZ2+qIMvlJflRBZscfrdy1BSMby1qR69erJ/rxN7k6Ze9c/idBkK/tamfOnf+rL072QWbHTKrBN+hPylJbUa90y63VeqHLlEqFJ81cFbahNr4TF/M0YlVaeiqaoMWulTpClP/1yikn2VHyF/aUmtwel0smfPHo4cOYLT6cTpdJKRdgYAR06u6o+F0wmHDsHhw1hsrhGjvxkN3mR1NDlZPW8vNDwLC8OhQ1hPpAKyg5d4x1ubOnv6tOtcfoFX/bPaC/XPz3LreZOVO1UO+NK/FEDqnz8hf2lJrSE/P58ePXrwyCOPYLVasVgsfPvF566TO3aCxeL6v8UCd9wB99+PtcCVksLfduV4k9X/TRivnncUWIoKF8rL8tp8QDokS7zjrU19tuBDAMS+/d71z2ID/K9NeZPV5PFl65/1pTmA/w0G/Zlq+aXj4+NRFMXj8+STT3qUOXLkCCNHjiQwMJDIyEgmTJiAtXi+JmDnzp3069cPs9lM48aNmTlzJkL4mJuW+AeFW5/tPtJPyCXCYjiLRs3ellTdu5hkBy+pMKKcnXFS/4oQFdM/KSv/odqc3GfOnMk999yj/h0UFKT+3+FwMHz4cKKioli3bh1paWmMHTsWIQTz57tG1VlZWQwaNIj+/fuzadMm9u3bR2JiIoGBgTz22GPVVU1JHaPc6OQyDlYxBBqnA6dG690HROc2sPxrtk9SdcqN5G6XuUCLU5b+WXTSwPI3qs3ACg4OJjo62uu5ZcuWsXv3bo4ePUpsbCwAc+fOJTExkVmzZhESEsLChQspKCjgo48+wmg00rFjR/bt28e8efOYNGmS3Nbqr5QXyV2myvFA53Ri1WjlCFpSPZQX20nOYHlQlv65MynIwaD/UG2/9IsvvkhERARdunRh1qxZHst/GzZsoGPHjqpxBTBkyBAsFgtbtmxRy/Tr1w+j0ehR5sSJEyQXc96V+Bnl5SKUudA80LrlJUfQXpHuDJWkcNm5vEwKUv9clKV/Vql/fke1zGA98sgjdOvWjbCwMDZu3MhTTz1FUlIS//3vfwFITU2lUaNGHteEhYVhMBhITU1Vy8THx3uUcV+TmppK8+bNS32vxWLBYilyJszKyqqOx5HUIsqNjixTdXigc/vMeHnZSx8sF9KdoeKUr39yBqs4bv3zamBp3YFG5RK9v+DTwJo+fTozZswo8+JNmzaRkJDAo48+qh7r3LkzYWFhXH/99eqsFuB1iU8I4XG8ZBn3iNDX8uDs2bPLraOkjuMeQftM1SENrOKUOYMllwgB6c5QKcrLr6f6QEqjAYr0Tw5wJFCGgTV+/HhuvvnmMi8uOePk5rLLLgPgwIEDREREEB0dzV9//eVRJj09HZvNps5SRUdHq7NZbk6dOgVQavbLzVNPPcWkSZPUv7OysmjatGmZdZbUXvR6PRMmTODw4cNq+olLe1zCXsAWHumZquOWW1ypOpz+6eTuTVYjr7uOXe7ArJpiql0oL2tAG8D/ZFWSF198keeee46mTZtyww038MQTT2AwuPxjynNn6N+/v093hqeeeork5GSvs+11AW9tqlfvXuwG7CEh3vXPTwc45emfw5v+mVsC/icrf8angRUZGUlkZGSVbrpt2zYAYmJiAOjZsyezZs0iJSVFPbZs2TKMRiPdu3dXyzz99NNYrVa1s1u2bBmxsbE+DTmj0ejRyUnqNgaDgeeff94j/cTgYUPZu8OJPTLKM1XHQw+5UuUkuw4Z9f41gvYmqzvvu4/J66xgA3vxGYVCeVl2ZMJh4dcdvHRn8I23NjX86qvZvc2BPSTUu/7tKbzWz9pUVfTP+k8mHBIyVY4fcc6/9IYNG3jllVfYvn07SUlJfPXVV9x3331cffXVNGvWDIDBgwfTvn17br/9drZt28bKlSt5/PHHueeeewgJCQFgzJgxGI1GEhMT2bVrF9999x0vvPBC/Ztyl1QKd/oJu4/zRak6ZKcFRQpddqqc+mWMTp8+vZTjesnP5s2bAXj00Ufp168fnTt3Zty4cbzzzjt88MEHpKWlqfc7X+4MoaGh6qeuzLRr1VQ53p/LKjeZeFCW/llkqhy/45yd3I1GI19++SUzZszAYrEQFxfHPffcw+TJk9UyWq2WJUuW8OCDD9K7d2/MZjNjxoxhzpw5apnQ0FCWL1/OQw89REJCAmFhYUyaNMljCVBSv3E6nRw+fJhTp07RtGnTwlQ5aUAYDpvdM1VHSgrk5mKxxwP+18F7k1XqiRMo9hDAVDpVR0oK1hwAc73r4KU7Q/XgrU2lp50GwnE4HF71z2pvAvif0VAl/csWQIDfycqfOWcDq1u3bvz555/llmvWrBmLFy8us0ynTp1Yu3btuVZJUkfJz8+nU6dOAHz//fcAvPbif2hw64s4U0+CJRgCAlypJ264AQDrSysB/zOwvMnqwbvu4tJ73uVseGMchSlMAFVe1kH3Q7cR9a6Dl+4M1YO3NjV7+jTC75yPIzMLLIGl9e+5XwH/m0EuV/+sXvTvijuhx2i/k5U/I39pSa1GiPIiuctt4sVRCnd9Ob2cs+hcxoC/GaNupDtDFSictRI+nssd6Ndf21RJNG79K2MXr78lxvZnqi2Su0RyXlADHcowDRVBW0YH7+/bxKU7Q1VwNSSpfxXDbWB5j4NVGCZFhrTwG6SBJanduOPKaL03VZkqxxNNobN1WQaWv8pKujNUgcIBjrMcA6u+bZyoKlr3jJ+Xc1adO9Cof+qfPyJ/aUmtRrg7eHztYpIdfHE0Tt9LhEUjaKn2kgrinhH1skToRMHulDNYxVHKGOCouQilrPwG+UtLajfuDt5HJHeL9MHyoGiJonQPL3MRSiqLO/yEtxksd249kG3KTYWWCKWs/Ab5S0tqN8L3EoWgmA+InJUBipYIvS1RqE62crZPUlHK0D93ewKpf27cPpDCiwa6DVIZaNR/kD5YklqDTqdj3LhxHDt2DI1Gg1arpfflfdgDOAxGcDuHarVw3XVY7Q71Wn/bmeNNVoOHDeOs3qXSHi9Et7wiGwJyBC3xjrc21bdfP/4BHDpdKf2ziKLXh15bz3ZPloNv/XMZUQ5v+tfIFfbD3/oqf0YRwstaQh0lKyuL0NBQMjMz1S3VkrpFdnY2K1euxGw2YzQaOZJewNS/HARrBTuvCgOTyVWwoIDs9Cw6rXONGP99bigmP0uXU1JWFouF2X9ZSM7T8m5CAEOaF9OBggKGrMpgby4sHNeD3hdVLW5UdeLP+lpbn71km0rNLODJDQ50iuDACE/9O3Y6iz5/OjHqNOx9fljNVrwG8KZ//9loISlXy3uXBDI4PriocEEBN6zNYFMmvH1rN4Z1iqm5ikuqRFV0VprSklqNRk3VUfqctZgnt1yicOGWl7dRU27hhF+Awb8MUUnVcfu2exuG58v2VAr3PJ7Tmw9kYX8lZ7D8B/lLS2oNQgjOnDlDZmYmQgiEEGRnZhaeo6iXFwLS07FkuBLm6rUKGo1/LVF4k1VGejrC6kooXDxTh1teeTbXwUCj9AyQlKYs/XOglNK/3IxsAAIM/teeytM/pxf9y7W6LFJ/lJe/Ig0sSa0hLy+PFi1akJiYiMVioaCggMkPPwyAw2aDggJXwYICGDmSgomuwI4mP3Ta9iaru265hUYHDgDgtFqLChfKK6/AdczsZ0upkorhrU1NvO9e9bzIz3f9x92eZs4CINDof+2pXP2zedG/02cBOePnT0gDS1LL8Z2qI89gBuSMTHHUQKMljjsUDQV6l/+MlJekwhRb6irZpnINrvYkZ2SKKCvQb55eysvfkAaWpHZTRiTpXHeH5YcjaF9oC3M3luzg8/RFiYblCFpSYUSRWVXSDzJP7x7gyPbkRoPvVFV5BvcAR8rLX5AGlqRW497k6i3ZszqDJUeEKr4iSbtlpVX8NxehpAoUM7BKtik5g1UaX5kUrBodtsK4YVJe/oPsaSW1m2IdfMmIIkUdvBwRunEvUThKHFdn+wxaFC/LrRKJN0QZS4RFAxypf25U/Ss1wDGp/5f9lf8gDSxJ7aZCSxRyROhGjSTtYwYrUG4Rl1SGMmawVJ8iqX8qvvQvt7CvMmgV9DKkjN8gf2lJ7abYfmffTrZyROhG8ZELTZ3BkjsIJZVBlKF/7k0TUv9U3PpXerbPrX/yletPyKGHpNag0+kYM2YMKSkpRaly+l7O3sLzTk2xVB3DhpEb0RHwTx8sb7LqN2AAZwwuPw9n8bhgWi15vS4HpIOtxDfe2lTffv3YU3jeqXjqX150K8A/fYp8658BAIdSQv/6DQCkMepvSHNaUmswGo288847TJgwAYPBgMFgYPyjE9XzTr2r88JggGeeIbdnH8A/lwi9yeqRyZPh4osBcGqKycRgIPf6GwEIMOq93U4i8dqmJjzxuHreWZhnT9W/Tl0A/zTafetfZwCc2hL6d0ciIJdT/Q1pYElqNcXdsUtNuxd6cvtjB+8LBe9xsPLsruNyBC2pDGXrn6tNmf1wBssXbv0r5QNpd/0rl+j9C2lgSWoNQghyc3MpKChQ009Y3NHbAYezWKqO/Hy/Tj3hTVYF+flgswHFZOUqTG6+K7K09AGR+MKX/qlGe0n9c6de8kOj3Zf+Cbf+iZL650qh44+y8mdkbyupNeTl5RETE8Mtt9yipp+44/rR6nlRPFXOoEHk/b4e8M8ZLG+yGnPddTTbuBFA7egBKCgg7/0PAQiUGi/xga82pXUUBq8tcBkJqv798y/gnwMcX7KKK9Q/Z0n9m/MKAAH+11X5NbK7ldRuygjTIAMdlkbrYxdTbmGYhgCDVHlJ5fAVWy1PLyOTl0QjvEdyL9rFK/XPn5C/tqTWo/jotNQ4WHLaXcVXmIZMUxAAgdIHRFJJNL7SL8kBTil8ZVLIdxujUv/8CmlgSWo9vhIYqzNYcmeOitaHMbovMg6AluGmkpdIJGXi1j8PtyIg3RwCQLBJ6p8b3zPIbmNUvnL9CflrS2o9WqePaXeZqqMUGi8jaKcQ7G7YHID2DQNqolqSOozbaHBQ1KhOBkWQZQpCq0CzcNmm3PhaIjwT2ACAUDkY9CukgSWp9fha9so2BgJyiaI43pLNHs51kmsMwGiz0DLcXDMVk9RZvC177Sk02FuEmzHJZS8VXwbWniiXvFpHSv3zJ6SBJan1qEsUxY6lmUM4GxCKAjSLkCNoNxovxuiuLJcPTZszh9FpZKJnSeXwZjTsiYoHoK00GDzw5s5gcwr2Fy7Rt4uSfZU/IYf+klqDVqvl2muv5eTJk2r6ict69eK4Ow6PO/2ERsOeQdcCENfARJAfTrv7klWK0RXtXhRL1bEl3dXddxXZrjQnEokXfLWpU4XnnUrheFyjYXenywBo1yioZipbw/iS1Ql3qpxiIVoP5YFVpyfQYaGpXE71K/zvzSSptZhMJj7++GNWrlyppp+YPHUqE9fawAEOd6oco5Hdo++A3QW0jwmu2UrXEL5k9dmuPDhdLG8jsDndNYOVMDABTNLJXeIdX23q8d+tYAdnofEgDAa2NG4HBYKLm4XVcK1rhvL0TxQbyOzOcxlbbcOMaMxyxs+fkEuEklqPeyzosUSR6TIa/HUE7Qu3rNwxi3JsTnZnuPJ0JITI5UFJ5XGvKrujkx/Lc5BSINAp0K1paA3WrPah6l+xvmprmivoaBepf36HNLAktR53B1/cr2F7hsuE6OinM1i+cMvKvaX+aK4DJxBuUIgxyQ5eUnncrcbdpv467TIYOgeDWTq4e6AOBosd25rmSlPVTRpYfoc0sCS1htzcXEJCQrjuuusoKCggPz+fUUOHYsrIAMBZmConLSOXpFxXF9Yt3D9XuX3JqtVPPwHgsLlmrc5aXHKKOJEMw4ZBbm5NVVlSy/HVpgLPnAGKUuUkp7v0sMOaxX7bnnzq32KX/olC/cu1O9lTOIPcbfztfisvf0UaWJJaT8mdcVsLfYpanz5MqAxy6IGmRMyws1bX3+F5WTVVJUkdR9W/wr/PWl2NKyIvs4ZqVHtxh7Rw91XHCmeQw/IyiclOq7mKSWoEaWBJaj3uQIfuJYrdhWEHOqfuq6kq1Vo0JSJJu2ewwvOlgSWpGpoS+pcujXaflMykkOaeQZbGqF9SbQbWkiVL6NGjB2azmcjISEaNGuVx/siRI4wcOZLAwEAiIyOZMGECVqvVo8zOnTvp168fZrOZxo0bM3PmTIQoEbFN4neUjC3jHkFHyxFhKYo6eJeM3AZWmHwZSqqIO5OCQ50Vdf0nTBrtpSg5wEm3uGQlBzj+SbWsryxatIh77rmHF154gQEDBiCEYOfOnep5h8PB8OHDiYqKYt26daSlpTF27FiEEMyfPx+ArKwsBg0aRP/+/dm0aRP79u0jMTGRwMBAHnvsseqopqSOUjKSu+zgfVNyieJsYQcfkS9H0JKqoeA5wEm3SaPBF0WpqgoHOFY5wPFnztnAstvtPPLII7z88svcfffd6vE2bdqo/1+2bBm7d+/m6NGjxMbGAjB37lwSExOZNWsWISEhLFy4kIKCAj766COMRiMdO3Zk3759zJs3j0mTJqEocgeGv1Jy2t3daUXITqsUvpYIZQcvqSolI7mrAxzZpkpRegZLLtH7M+e8RLh161aOHz+ORqOha9euxMTEMGzYMP755x+1zIYNG+jYsaNqXAEMGTIEi8XCli1b1DL9+vXDaDR6lDlx4gTJycnnWk1JHcbXEqGcwSpNSX+1s9IHRHKOFE+2LoQg3SpnsHyhKTWDXDjAkbLyS855BuvQoUMATJ8+nXnz5hEfH8/cuXPp168f+/btIzw8nNTUVBo1auRxXVhYGAaDgdTUVABSU1OJj4/3KOO+JjU1lebNm5f6bovFgsViUf/OypKNuC6j1WoZPHgwaWlpavqJrt27Y1HjYLn+o3bwzZv4beoXX7I6UiJVh9shOax5Ywi6xG/lJSkfX20qrzC4mlNRyLIJ1Xho0KGV37YnX7I6atADIEroX3jDMLhE6p+/4dPAmj59OjNmzCjz4k2bNuEsHN0888wzjB49GoAFCxbQpEkTvv76a+677z4Ar0t8QgiP4yXLuB3cfS0Pzp49u9w6SuoOJpOJb775xiP9xLOzZjFtgwUKwKHXI4RQZ7DCH77fb1O/+JLVz3tz4QQ4Na7JaXUX4YP3gjbfb+UlKR9fbeq5Py2QD069XjUYArVgmj7Nb9uTL1kt/jcXUopSVan6N/oaaDDUb+Xlr/g0sMaPH8/NN99c5sXx8fFkZ2cD0L59e/W40WikRYsWHDlyBIDo6Gj++usvj2vT09Ox2WzqLFV0dLQ6m+Xm1ClXmtGSs19unnrqKSZNmqT+nZWVRdOmTcuss6Tu4V7HFgJy7YLCPp5wfY1VqdZSMq1QdqFDcohe8QwvLZFUEDWTgii25GWQEX68UTKtUJG8pA+xP+JTSyIjI2nbtm2ZH5PJRPfu3TEajezdu1e91mazkZycTFxcHAA9e/Zk165dpKSkqGWWLVuG0Wike/fuapm1a9d6hG5YtmwZsbGxpZYO3RiNRkJCQjw+kvqHonZaRVPuJg2YtbLTKolSLK2QUwjyC9dzzDopKzcypEzlKG60ZxU32CWlUNMKFf6rOrkbpbz8kXMehoSEhHD//fczbdo0li1bxt69e3nggQcAuOGGGwAYPHgw7du35/bbb2fbtm2sXLmSxx9/nHvuuUc1isaMGYPRaCQxMZFdu3bx3Xff8cILL8gdhH5Ebm4u0dHR3HzzzWr6iVuuvpqQpCQAnBZrUeC+jNNw7bV+m3rCl6za/O9/ADjsDgqKZZwNuPZqv5aXm0WLFnH77bdz55138vfff/PHH38wZswY9bw7pExubi7r1q3jiy++YNGiRR6hYtwhZWJjY9m0aRPz589nzpw5zJs3ryYeqdrw1abC9rsC+josFvLtrjYV8O8uv25PvmTV+pOPAXDaXcGQVYN0XKJfy8tfqZY4WC+//DI6nY7bb7+d/Px8evTowapVqwgLCwNcDoFLlizhwQcfpHfv3pjNZsaMGcOcOXPUe4SGhrJ8+XIeeughEhISCAsLY9KkSR5LgJL6T15ensffFqsVvXtnHILMQv+rkPxsKLbBwR/xJitTsV2EefYiA8ucnUnRuNo/kSFlysdbm9Kp+lfUpsyWfKl/XvWvaBehEEKVV0BOlt/Lyx+pFgNLr9czZ84cD4OpJM2aNWPx4sVl3qdTp06sXbu2OqokqUcUD55pKeywAm35NVmlWou2WBwe9/KgSQMaPzeuoHRImdTUVLp06cKcOXPo0KEDUH5Imf79+/sMKfPUU0+RnJzsdcdzXaYoeGZRmzLbpLHgjeL6Z3UWuT2arQU1VidJzSE9FSW1nuKBRvNkB18mxY1RdfQs/a8Az5AyU6ZMYfHixYSFhdGvXz/Onj0LUOGQMiXLFA8p4w2LxUJWVpbHp66gKZYqR10itEmDwRvFg7LmF1+il/LyS6SBJan1FI+OnOvu4OWI0CvFjVG3rMz1PPTO9OnTURSlzM/mzZtLhZTp3r07CxYsQFEUvv76a/V+5yukTGhoqPqpS7udiwf6LRrgSP3zhlJsts89wDFoUJdZJf5FtSwRSiTnE02xSNJyBF02xY1RVVb1fLelDClzftEKl8N2cb++ADmD7JXiS4Sqv1o91z+Jb6SBJan1FE8/IZcIy6a4MeqWVX1fIoyMjCQyMrLccsVDyvTp0wfwHlJm1qxZpKSkEBMTA3gPKfP0009jtVoxFEbOr0hImeI+W3WJ4svO7p2pcoDjHW9LhAH1fAZZ4htpYElqDRqNhj59+pCeno6iKGg0Gtp36EBu4UK2U1GKOq2wYOjUCTT+ucrtS1aHC1N1OCg226BVoEsXcDj8Vl7gGVKmadOmxMXF8fLLLwPeQ8q8/PLLnD171mtImRkzZpCYmMjTTz/N/v37eeGFF5g6dWqd3kHoq01lF0uV425TpshwqX/e9E/neqWWmsGS+ueXSANLUmswm838/PPPrFy5Uh3xPz93Lq9sLoBsEDo9eXmFRsOgKyGxH5jNNVzrmsGXrP44lAOHQSiaoiVCgxbeeAOys/1WXm5kSBnf+GpT87cUQBY4dboio/26ayBqmN+2J1+y+v1gDhwBp6IpMrD0Gql/foo0sCS1HvecgGtnnGsKXkYm944adZtiy6lSVioypEzlUbMDFF/2km8Or2i8ykrqn78i5ysltZ6i9C+imF+D7LS8UZRWSPiNk7vk/FI8VU6+dNwuE48BjpSV3yMNLEmtITc3l+bNmzN27Fg1/cSdN95IxNatADgttqJO643X4Kab/Db1hC9ZtX31FQCEw1m0nIMDRozwa3lJysdXm4rc6NpV6bTaimZFX3jer9uTL1m1mf8aAE6Ho2iAozil/vkpcqJXUqtIS0vz+DszKwuT07VNvPioMCDzLNShYI3nA2+yahBdFBRSfRlqFcjIuNDVk9RBvLUps7fQHxlpUv+8yCo0plhQ1uKz7VL//BJpYElqPcXDNKidlgw06hU1TAPFXoZSyyXnQFHoD0Ge3XVMBhr1jtZb3kbpg+W3yCVCSa1H6yWBsYyD5R2Nl0jS0gdEci5ovMXBkgMcryjeBoMyDpbfIg0sSa1HETIXWkXxlrdROrlLzgWNl1kZqX/e0RYP9CsHOH6PNLAktZ6iXGiiyGiQHbxXihujFtUHqyZrJKnruA0se7FZGZNdziB7w9MYLQwpIw0sv8WvvDMcDgc2m62mq1Hv0ev1aLXV91b3mJVRlwilgeWN4ol53QaWUSM7eEnVcbepfIfLcACZi9AXCudniVAIgd1ux+FwnPvNJGVSne8vvzGwcnJyOHbsmJr1XnL+UBSFJk2aEBQUVKnrNBoNXbt2JTs7W00/0aJlSzILgzvZULAWJqUPiG8K+Q38NvWEL1kdVlwdgwAshX2xUadA27YyVYekTHzrn+t8vrPIUDe1bA52m9+2J1+yOqJx6Z9rk4mrrPkc9c9qtZKSkkJeXl41PoHEF1V9f3nDLwwsh8PBsWPHCAgIICoqqk7nC6vtCCE4ffo0x44do1WrVpUaCZjNZn777TeP9BNz3nyTD//Oh7OQhxZw9Vrm11+D/By/TT3hS1a7jmbDAXCgYHG6BhMGkwH++1+ZqkNSJr7a1Mc78iEN8gs9SjSA7v33/Lo9+ZLVjiPZcNCVt9Gtf8Zz0D+n00lSUhJarZbY2FgMBoN8f51HzuX95Q2/MLBsNhtCCKKiojD7aYdwIYmKiiI5ORmbzVYtU63u7iSv2Oy4UfoVeUWjFO0itHosEcqZW0nVcL/P3UteRulT5JPiaYUs1SAvq9WK0+mkadOmBAQEVEcVJeVQne8vvzCw3EjL/8JQ3XLWlOjgDRrQyN/SK8VTddic8oUoOXfcfkUFhQMcg3+uClYIDcUGOM7qG+Bo/HQptiaozveXXxlYxSkoKDgvDu96vR6TyVRuOUVRyM7OrpZ13rJITEwkISGB8ePH884775Cfn8+jjz56Xr+zquTl5dGxY0fy8/N5//33EUJw/x13cHW3G6HLMAqsruVBgwa4/npwOuGff8APR3a+ZPWWMRJufRmnU2Ap9Jkx2K1w/U1+LS9J+fhqU6MvHgXdR5Jvc1lYRql/PmU139QIxvzHpX9ug9RuhetvqVZ5yfdX3cAvDayCggLWrl17XpwGAwIC6Nu3b4Ua6YXm/vvvr+kqlIkQgiNHjnj8ffr0aUILU+Xku522NQqkproLXehq1gp8ySomJgxwJXu2FG4IMCrC7+UlKZ/y9c89I4rftyef+tc4Eig5g0W1yku+v+oOfjnvaLPZyMvLQ6fTYTabq+2j0+nIy8ur8Mhizpw59O7dm9atW/P555+rx2+77TYSEhLo3LkzI0aM4NSpUwDs37+f3r17c/HFF9OpUyemTJmiPs+TTz7JpZdeSpcuXbj55pvJ8JL7avr06Tz++OMAfPTRRwwZMoRbbrmFTp06kZCQwKFDh9Syn3zyCT169KBbt27069ePXbt2VVXc50zxbeJQ2GFJvOKWlagmHxCJpKT+GWTYD59o1LyNQvWBrO4lVfn+qjvvL79+Ven1enUHSHV89Hp9pb5fURT++OMPli5dysMPP8zRo0cBePXVV9m8eTM7duygT58+zJw5E4A33niD4cOH8/fff7Nz504mTZoEwMsvv0xQUBAbN25k+/btdOjQgWnTppX7/X/99Rf/+c9/2LlzJwMHDuTFF18E4I8//uCLL75g7dq1bN26leeff55bb721Us9WnShqBy8NhvJwxwxzgBrSQvrMSM4Ft9FQ4J4Rle3JJ8XTCqkzyOfJIJXvr9r//vLLJcLawrhx4wBo0aIFffr04ffff2fMmDEsXLiQTz75BIvFQn5+PtHR0QD07duXJ554gtzcXPr168fAgQMB+P7778nKyuKbb74BXDtPWrZsWe739+nTh7i4OAB69uzJ/PnzAfjhhx/4+++/6dGjh1r29OnTWK1WDAZD9QmggmhLdPDSYPCNO5J7QfEdl3LGQXIOaEq0KTmD5Rtt4XKqKLaLt772V/L9VT7SwKpFKIrCunXreOONN1i/fj1RUVH8+OOP6ghg9OjR9OrVi+XLl/PGG2/w6quv8vPPPyOE4K233mLAgAGV+r7i6+xarRa73eVELoTgrrvuUr+3pnF38B4+WBKvuHOh5TuLjskZB8m5oC4RFvcpknjFPdvuhKI4WH4y4y7fX6WRqlKDfPjhhwAkJyezbt06+vTpQ3p6OiEhIYSHh2O1Wnn33XfV8vv376dhw4bccccdvPTSS/z5558AXH311cybN091eszLy+Off/6pcr1GjhzJxx9/rE75Op1ONm/eXOX7nSsa1Wgo5mQr8Yp7m7i1mIFVX0fQkguDtsQAx+AnBkNVcBujdlH/l+jl+6t8/HoGq7q3uVb2fkajkd69e3P69Gnmz59P06ZNiYmJ4dNPP6Vt27Y0adKEXr168euvvwLw9ddfs3DhQgwGA0II3nnnHQCefPJJZsyYQY8ePdQYHv/3f/9Hhw4dqvQcffv25YUXXuCaa65R8zcOHz6chISEKt2voiiKQtu2bcnNzVX/btKkCRlqHCzXvwaNAvHxrm3PfhoPy5esjlg95WHQgKLR+L28JOXjq02ll9A/owa/b0++ZHXU5pKHpfgMsvb86J98f3mnpt5f3lBEPUrOl5WVRWhoKJmZmYSEhKjHCwoKSEpKonnz5phMJr/d5nqhKCnvypCdnc3KlSsxm80YjUYsFgvf7s7j15MGArWQ64ABMUY+TDC7Uk/07euXcXjAu6yOpWby3J5AtUywXmHntY2goKDWycuXvvoDtfXZvbWp7/fk8kuqUS0zvImJN7uaal17utB4k9XhlExe+DcQneKaxQLYO6oRRpulSvLy1pfK99f5xdf7qyo665czWCaTib59+9ZooDZJxVG8jaAlXik5QJb+apJzpWQTkkv0vnHrn73YtEV1LxHK91fdwS8NLHA1UtmQ6gbF07+A9AEpi5KSkS9DyblSqk1Jo90nJW0pg+b8pGiT76+6gd8aWJLaR15eHpdeeim5ubnMnz8fIQSP3HMPN8X3hd5j1HJG4YTbbnP5NGzZ4pdLFL5k9d9cB9z7vlrOqFFcy4N+Li9J+fhqU7c26wWX36GWMwiH37cnX7J6Nx8Y945azqiV+ufPSANLUmsQQvDvv/96/H3s2DEaNrV7lDNqgORkd6ELV8FahC9ZXRQc4VHOoFVcMvJzeUnKx1ebahQr9a8kvmTVMqShRzmjRuqfPyO9WSS1HneYBjf1ddtzdaAp0YHL5RzJuVKyTclAo74ppX9yid6vka8qSa2ndKclO3hflDRGZQcvOVc0wuHxt2xTvtFQcjAo+yp/xn+XCK1WsNvLL1dZdDqogXQy9Rl3oEM3chehbzQlZSWNUck5ImdFK4621ADnPMlKvr/qBP5pYFmtsHEj5ORU/72DguDSSy94I/3oo49YvHixms+pPqEIOSqsKCWNUSkryblSeomwhipSB1AuhKzk+6vOcM4//5o1a1AUxetn06ZNarkjR44wcuRIAgMDiYyMZMKECVitVo977dy5k379+mE2m2ncuDEzZ87kvMRBtdtdjdNggODg6vsYDK77no+RhR9TegRdQxWpAxgcnrFx5HKO5FzROEssEUqj3SclZ5DPywBHvr/qDOf8qurVqxcpKSken3HjxhEfH6+Gpnc4HAwfPpzc3FzWrVvHF198waJFi3jsscfU+2RlZTFo0CBiY2PZtGkT8+fPZ86cOcybN+9cq+gboxFMpur7GI3lf2chiqIwffp0evfuTevWrfn888/Vc5s2bWLAgAEkJCTQrVs3Fi1aBIDdbmfIkCEkJCTQoUMHbr31Vq/RfI8fP84ll1zCggULzl1GFxBFUWjWrBlRUVHq31FRUWTixQcrOhoaNvTrVB3eZHXKUXLHl+KSkZ/LS1I+vvXPE4PUP5+ySikVlPU86p98f9V6znmJ0GAwEB0drf5ts9n48ccfGT9+vBpgbdmyZezevZujR48SGxsLwNy5c0lMTGTWrFmEhISwcOFCCgoK+OijjzAajXTs2JF9+/Yxb948Jk2adF6CtdU0iqLwxx9/cOjQIS699FL69OlDcHAw9913H0uWLCEmJoYzZ87QvXt3evfuTaNGjfjss8+IiIhACMGDDz7IW2+9xeOPP67ec8eOHYwZM4ZXXnmFQYMG1eDTVZ6AgAB27drFypUrMZlMGI1G3v3kE1bsz4FjReUMJj18840r9YSfxpTxJaudGRnotwtswqUvBq3i6jz9XF6S8vHVpn4/mANHisoZpf75lNXutAzYUVTOoKm/+iffX+VT7T5YP/74I2fOnCExMVE9tmHDBjp27KgaVwBDhgzBYrGwZcsW+vfvz4YNG+jXrx/GYlb0kCFDeOqpp0hOTqZ58+alvstisWCxWNS/s7Kyqvtxzivjxo0DoEWLFvTp04fff/+dBg0acOjQIYYNG6aWE0Kwd+9eGjZsyCuvvMKSJUuw2+1kZmbSt29ftdyOHTu49tpr+eGHH+jUqdMFf57zRUk/UblEUTYmDdjUtEJSVpJzw6CRYRoqis6P0grJ91f5VLuB9cEHHzBkyBCaNm2qHktNTaVRo0Ye5cLCwjAYDKSmpqpl4uPjPcq4r0lNTfVqYM2ePZsZM2ZU8xPUHIqiIISgc+fOrF27ttT5Tz/9lN9++421a9cSHBzM66+/7lGuSZMm5OTksHr16nrTQAH0JRay5c64sjFqILvQwDKX7PElkkqi86p/MmCmN3QKKAhEYYIhfzJG5furND59sKZPn+7Ted392bx5s8c1x44d49dff+Xuu+8udT9vS3xCCI/jJcu4Hdx9LQ8+9dRTZGZmqp+jR4+W8ai1jw8//BCA5ORk1q1bR58+fejVqxf79+9n1apVarnt27djtVpJT08nIiKC4OBgsrOz+eijjzzuFx4ezooVK/jmm2+YNWvWhXyUaiE/P59+/frxxBNPqLOTjz/0ELFvzvcoZ7DbYNw4mDAB8vNrqLY1iy9ZxY0dizk9TS0XoCtM1eHn8pKUj6821eTllzzKGaX++ZRVfOJYl3wKUVPl1EN5yfdX+ficwRo/fjw333xzmReXnHFasGABERERXH311R7Ho6Oj+euvvzyOpaenY7PZ1Fmq6OhodTbLzalTpwBKzX65MRqNHkuKlabY8mK1UMn7GY1GevfuzenTp5k/f7466/fTTz/xxBNP8Oijj2Kz2WjWrBnff/89d9xxBz/88APt27encePGXH755Rw/ftzjniEhISxdupRRo0bx5JNP8p///KfaHu9843Q62bZtG+Ayrp1OJ4cOHqRtizCPckYN4E5TUSLujL/gS1adAVNBLgRFAhDgTpXj5/KSlI+vNtW+seeW/QCtbE9l6p+tgAKdS2YGDedP/+T7q9bj08CKjIwkMjKywjcSQrBgwQLuuOMO9Hq9x7mePXsya9YsUlJSiImJAVyO70ajke7du6tlnn76aaxWK4bCGBzLli0jNja2lCF3zuh0rngfOTmumCLVSVCQ6/4V4MEHH2Ty5MmljickJLB69epSx00mEytWrPB6r8TERNXvLSAggKVLl1a8zrUco91T8c312K+hOjDZitq0XCKUnCsmu2cfaZZL9GVSXF4B50P/5PurzlBtPlirVq0iKSnJ6/Lg4MGDad++Pbfffjsvv/wyZ8+e5fHHH+eee+4hJCQEgDFjxjBjxgwSExN5+umn2b9/Py+88AJTp06t/h2EBoMrmJqMhFsnKNnBB8gOvkyKy0u+DCXniqnUAEe2qbI47wMc+f6qM1SbgfXBBx/Qq1cv2rVrV+qcVqtlyZIlPPjgg/Tu3Ruz2cyYMWOYM2eOWiY0NJTly5fz0EMPkZCQQFhYGJMmTWLSpEnVVUVPDIYabUjnJYBqPaV4hwXnaVRYjyj+QnTJSrY1SdUxltK/GqpIHcFD/86XMSrfX3WCalOVzz77rMzzzZo1Y/HixWWW6dSpk9fdBxL/pvQIuoYqUkcw2kuOoGVnKKk6cga5chR3cpdL9P6NTDoiqfVIH5DKYbZdgBF0HaROpvWqBZQc4MhUVWVT3GdU9lX+jZzsldQqIiIisNmKRoChISHk2T3z65m1QIMGfruDyY03WZ3JzsaoFL3o1eVUKS81rVdxnn32WVasWFEqrVdUVBTr1q0jLS2NsWPHIoRg/nxXuBB3Wq/+/fuzadMm9u3bR2JiIoGBgR7pv+oi3tpUbq5nOhNFUWR7wrf+mSiSi9Q//0YaWJJaQ2BgIElJSR7pJxZ89RV/n82Av4vKaQICYPFiV+qJwMAaq29N4ktWGzIy0B83gyvCCSatAmaz38sLZFqv8vDVpjZlZMC2YgVleypb/5LNkO4qZ9YqYDb5vbz8FTnZK6n1SDeGymHUeJnBkpSiKmm93GW8pfU6ceIEycnJXr/LYrGQlZXl8ZHUT0zFVE76YPk30sCqp0yfPr2U30hdpXi2CdldlU/xPl0aWL45l7ReJcsUT+vljdmzZxMaGqp+in+npH5R3EdN+mBVjfry/pIGVi3Afh7imcyYMcNnAz0f31cd5Ofnc9VVVzFlyhQ1/cSUxx4j+q671DIKuFJPjB8PkyfXq9QTlaEsWSlrf1fLmd2pOuqxvGRar+qhIvqngXrfnipCWbIK2PSnWk5NVVWP5SXfX77xOx8sIQT5Nsd5/Q6zXluuH4aiKMyZM4effvqJSy65hDvuuIMHH3yQ3NxcCgoKuP3223nqqafIycmhWbNmnDx5Er1eT7du3WjXrh0LFy7k0KFDDBo0iIMHD3rc+/777wdcTr0ajYZly5YxefJkQkJC2LdvH0ePHuWff/7htttu499//8VqtdKsWTM+/PBDGjZsCLjSHr322msIIdDr9XzzzTfEx8fz66+/8txzz5Gfn49Op+Pll1/2yIh+LjidTtatWwcUpZ/Y/c8/XFKsjEbBlXpi+3b3RdXy3XWNsmS1/OxZtZyaqqMey8sv0npdAKT+VZyyZLXs9Elo7ioXoFOqVV7y/VV731/e8DsDK9/moP3UX8/rd+yeOYQAQ/mitVgsrFmzBoDs7GxWrFiB0WgkPz+fXr16MWjQIBISEujQoQMbNmygQ4cOOBwOdUv58uXLGThwYKn7vvPOO7z77rusX7+eoKAg9fi6detYu3ateuzVV19V0yH95z//YebMmbzxxhusWbOGWbNm8fvvvxMTE0NenmsX0aFDh5gxYwZLly4lJCSEAwcO0K9fP5KTk0ulRzpf+FFy+iojyphpqY/U67RetYz635rOneJx6ExapVrD0Mn3V916f/mdgVWbuKvY1Ht+fj4PPvgg27dvR6PRcPToUbZv305CQgIDBw5kxYoVnDx5kiFDhrBnzx527drFihUruPHGGyv8fTfeeKNHg124cCGffPIJFouF/Px8dYfVkiVLuOOOO9QXTEBAAABLly7lwIEDpSz+o0eP0qJFiyrLoTLIDr58nIpc+S+LOpXWq5ZRzx+v2gnQKWArv1xdRL6/ysfvDCyzXsvumUPO+3dUhOKN5emnn6ZRo0Zs27YNnU7HqFGjKCgoAGDgwIE88cQTnDp1ilGjRtG4cWOWL1/Ob7/9xjvvvFPhepUcDbzxxhusX7+eqKgofvzxR2bOnFnm9UIIhg4dyscff1zh76xuNLKHLxenlFGZ1Lm0XrUIOYNcPsUHOGZt9RpY8v3loq68v/zOwFIUpULTnxea9PR0OnbsiE6nY+/evSxfvpwBAwYA0KNHD/79919OnTrFK6+8QuPGjRk+fDhNmzYlIiLC6/2Cg4PJzMz0aJQlvy8kJITw8HCsVivvvvuuem7kyJHcdddd3HvvvURHR6tTrIMHD2bGjBns2rWLjh07ArBx40YuvfTS6hRFmcj+vXyCrPXPkbY6kWm9qo5GamC5iGIyqu4BoXx/FX1fXXh/1b5fyk+ZMmUKt99+OwsXLiQ+Pl5tnAA6nY4+ffqQk5OD2WymQ4cO2Gw2r+vXbh577DEGDBiA2Wxm2bJlpc4PGzaMTz/9lLZt29KkSRN69erFr7+61vb79u3LlClTGDx4MIqiYDAY+Oabb2jVqhWffvop48aNIz8/H6vVSrdu3Vi4cGH1C8QHcnKmfMZt/I71w25hZHPvnZNEUlWk/pWPP84gy/eXdxRRjxJoZWVlERoaSmZmpuovAVBQUEBSUhLNmzfHZDLVYA39g6rKOzc3l4YNG+JwOPjyyy8xGAzceu21HLVa6fB/rhmFYJ3CzqEhMGiQa3fO0aMQFXW+HqXWUpasAoxGlMWLISzMVTg/v1bKy5e++gO18dkron8NDArbB0v9K0tWr155D+8nXANA8g3R56R/8t114fEl86rorJzBktQaAgMDSU1N9Ug/8fmPP7KqWKoORcGVqmPFCr9OPVGWrK7U6wkwm4sKS3lJKkBZbWp2po0Xjuh5p1cYmA1+357KkpU1xQTFo3hI/fNb5HYjSZ3gpoau2C9PdAyu4ZpIJP7HNZFO/h4aymVRhpquSq1nZIQr1lW7UDl/4e/IFiCpEzwbZ+f+Dg2IjzCXX1gikVQ7cgdvxWgfKPj9ymCiQgNquiqSGsavDKx65G5Wq6mqnAsKCrj++utJS0tj2rRpKIrCc888w3/278fQogXN5851rRFaLPDEE2C3w9KlEOB/HVlZstK1aAFz54Lbf0DKS1IBKtymZHsqV1ZN584Fdx7CapCX008j5tcE1Wkn+IWBpdfrURSF06dPExUVVe+DAdYkQghOnz6NoiiVjo7rcDjUHSNOpxOHw8G2LVvoA65UE+5OxumEDRvcF1Vb3esSFZaVq4Dfy0tSPlL/Ks6F0j+DwYBGo+HEiRNERUVhMBjk++s8ci7vL2/4hYGl1Wpp0qQJx44dIzk5uaarU+9RFIUmTZqg1VYsYJ1EIpFISqPRaGjevDkpKSmcOHGipqvjF1Tn+8svDCxwRYFt1aoVNls9zVtQi9Dr9dK4kkgkkmrAYDDQrFkz7HY7Dj+dMbyQVOf7y28MLHDNZMkXv0QikUjqEu4lq/OVlFhyfpBhGiQSiUQikUiqGWlgSSQSiUQikVQz9WqJ0L29Misrq4ZrIqkKubm56v8zMzPR6/UIQP0109NdO3LyiyUzzs4Go/FCVrNWUGFZQa2Vl1tP/TF8Sm3sq6T+VZz6oH+SylGV/qpe5SI8duwYTZs2relqSCSSSnD06FGaNGlS09W4oMi+SiKpm1Smv6pXBpbT6eTEiRMEBwfLWCG4LO6mTZty9OjRWpNQtq4gZVc1KiM3IQTZ2dnExsai0fiXt4Lsq1xIPasaUm5Vp6qyq0p/Va+WCDUajd+NhCtCSEiIVMIqImVXNSoqt9DQ0AtQm9qH7Ks8kXpWNaTcqk5VZFfZ/sq/ho0SiUQikUgkFwBpYEkkEolEIpFUM9LAqscYjUamTZuGUe5aqTRSdlVDyk1SGWR7qRpSblXnQsquXjm5SyQSiUQikdQG5AyWRCKRSCQSSTUjDSyJRCKRSCSSakYaWBKJRCKRSCTVjDSw6gDHjx/ntttuIyIigoCAALp06cKWLVvU84qieP28/PLLahmLxcLDDz9MZGQkgYGBXH311Rw7dszje9LT07n99tsJDQ0lNDSU22+/nYyMjAv1mNVOeXLLyclh/PjxNGnSBLPZTLt27Xj77bc97uGPcoPyZXfy5EkSExOJjY0lICCAoUOHsn//fo97+Kvs/IXZs2ejKAoTJ05UjyUmJpbqhy677DKP66qrXRw5coSRI0cSGBhIZGQkEyZMwGq1nq/HrVaqKrv33nuPK664gpCQEBRF8aor9Vl2VZHb2bNnefjhh2nTpg0BAQE0a9aMCRMmkJmZ6XHv8yI3IanVnD17VsTFxYnExETx119/iaSkJLFixQpx4MABtUxKSorH58MPPxSKooiDBw+qZe6//37RuHFjsXz5crF161bRv39/cfHFFwu73a6WGTp0qOjYsaNYv369WL9+vejYsaMYMWLEBX3e6qIichs3bpxo2bKlWL16tUhKShLvvvuu0Gq14vvvv1fL+JvchChfdk6nU1x22WXi8ssvFxs3bhT//vuvuPfee0WzZs1ETk6Oeh9/lJ2/sHHjRhEfHy86d+4sHnnkEfX42LFjxdChQz36o7S0NI9rq6Nd2O120bFjR9G/f3+xdetWsXz5chEbGyvGjx9/3p/9XDkX2b3yyiti9uzZYvbs2QIQ6enppe5fX2VXVbnt3LlTjBo1Svz444/iwIEDYuXKlaJVq1Zi9OjRHvc/H3KTBlYt5//+7/9Enz59KnXNNddcIwYMGKD+nZGRIfR6vfjiiy/UY8ePHxcajUYsXbpUCCHE7t27BSD+/PNPtcyGDRsEIP79999zfIoLT0Xk1qFDBzFz5kyPY926dRNTpkwRQvin3IQoX3Z79+4VgNi1a5d6zG63i/DwcPH+++8LIfxXdv5Adna2aNWqlVi+fLno169fqZfdNddc4/Pa6moXP//8s9BoNOL48eNqmc8//1wYjUaRmZlZTU9a/ZyL7IqzevVqrwZWfZVddcnNzVdffSUMBoOw2WxCiPMnN7lEWMv58ccfSUhI4IYbbqBhw4Z07dqV999/32f5kydPsmTJEu6++2712JYtW7DZbAwePFg9FhsbS8eOHVm/fj0AGzZsIDQ0lB49eqhlLrvsMkJDQ9UydYmKyK1Pnz78+OOPHD9+HCEEq1evZt++fQwZMgTwT7lB+bKzWCwAmEwm9ZhWq8VgMLBu3TrAf2XnDzz00EMMHz6cgQMHej2/Zs0aGjZsSOvWrbnnnns4deqUeq662sWGDRvo2LEjsbGxapkhQ4ZgsVg8lrJrG+ciu4pQX2VX3XLLzMwkJCQEnc6VLfB8yU0aWLWcQ4cO8fbbb9OqVSt+/fVX7r//fiZMmMDHH3/stfz//vc/goODGTVqlHosNTUVg8FAWFiYR9lGjRqRmpqqlmnYsGGp+zVs2FAtU5eoiNxef/112rdvT5MmTTAYDAwdOpS33nqLPn36AP4pNyhfdm3btiUuLo6nnnqK9PR0rFYr//nPf0hNTSUlJQXwX9nVd7744gu2bt3K7NmzvZ4fNmwYCxcuZNWqVcydO5dNmzYxYMAA1SivrnaRmppKo0aNPM6HhYVhMBhqbds5V9lVhPoou+qWW1paGs899xz33Xefeux8ya1eJXuujzidThISEnjhhRcA6Nq1K//88w9vv/02d9xxR6nyH374IbfeeqvH7IIvhBAoiqL+Xfz/vsrUFSoit9dff50///yTH3/8kbi4ONauXcuDDz5ITEyMz5ES1G+5Qfmy0+v1LFq0iLvvvpvw8HC0Wi0DBw5k2LBh5d67vsuuPnP06FEeeeQRli1b5rN/uemmm9T/d+zYkYSEBOLi4liyZInHoK8kVWkXdantnE/ZlaQ+ya665ZaVlcXw4cNp374906ZN8zh3PuQmZ7BqOTExMbRv397jWLt27Thy5Eipsr///jt79+5l3LhxHsejo6OxWq2kp6d7HD916pRqkUdHR3Py5MlS9zx9+nQpq70uUJ7c8vPzefrpp5k3bx4jR46kc+fOjB8/nptuuok5c+YA/ik3qFib6969O9u3bycjI4OUlBSWLl1KWloazZs3B/xXdvWZLVu2cOrUKbp3745Op0On0/Hbb7/x+uuvo9PpcDgcpa6JiYkhLi5O3WFaXe0iOjq61KxBeno6NputVrad6pBdRahvsqtOuWVnZzN06FCCgoL47rvv0Ov16rnzJTdpYNVyevfuzd69ez2O7du3j7i4uFJlP/jgA7p3787FF1/scbx79+7o9XqWL1+uHktJSWHXrl306tULgJ49e5KZmcnGjRvVMn/99ReZmZlqmbpEeXKz2WzYbDY0Gk8V0Gq1OJ1OwD/lBpVrc6GhoURFRbF//342b97MNddcA/iv7OozV155JTt37mT79u3qJyEhgVtvvZXt27ej1WpLXZOWlsbRo0eJiYkBqq9d9OzZk127dqlL0gDLli3DaDTSvXv38/L850J1yK4i1DfZVZfcsrKyGDx4MAaDgR9//LHUbNh5k1ulXO8lF5yNGzcKnU4nZs2aJfbv3y8WLlwoAgICxKeffupRLjMzUwQEBIi3337b633uv/9+0aRJE7FixQqxdetWMWDAAK9bozt37iw2bNggNmzYIDp16lRnt8xXRG79+vUTHTp0EKtXrxaHDh0SCxYsECaTSbz11ltqGX+TmxAVk91XX30lVq9eLQ4ePCi+//57ERcXJ0aNGuVxH3+Unb9RfEdXdna2eOyxx8T69etFUlKSWL16tejZs6do3LixyMrKUq+pjnbh3jJ/5ZVXiq1bt4oVK1aIJk2a1PpQA8WpiuxSUlLEtm3bxPvvvy8AsXbtWrFt2zaPsAT1XXaVlVtWVpbo0aOH6NSpkzhw4IBHOIfz3eakgVUH+Omnn0THjh2F0WgUbdu2Fe+9916pMu+++64wm80iIyPD6z3y8/PF+PHjRXh4uDCbzWLEiBHiyJEjHmXS0tLErbfeKoKDg0VwcLC49dZbvcZZqSuUJ7eUlBSRmJgoYmNjhclkEm3atBFz584VTqdTLeOPchOifNm99tprokmTJkKv14tmzZqJKVOmCIvF4lHGX2XnTxR/2eXl5YnBgweLqKgotV2MHTu21G9eXe3i8OHDYvjw4cJsNovw8HAxfvx4UVBQcD4ft1qpiuymTZsmgFKfBQsWqGXqu+wqKzd3SAtvn6SkJLXc+ZCbIoQQlZ22k0gkEolEIpH4RvpgSSQSiUQikVQz0sCSSCQSiUQiqWakgSWRSCQSiURSzUgDSyKRSCQSiaSakQaWRCKRSCQSSTUjDSyJRCKRSCSSakYaWBKJRCKRSCTVjDSwJBKJRCKRSKoZaWDVQ6644gomTpyo/h0fH8+rr75a4euTk5NRFIXt27dX6nv8HSkPiaRyyL6qZpDyuDDoaroCkvPPpk2bCAwMrHD5pk2bkpKSQmRkJABr1qyhf//+pKen06BBA7Xct99+65GRXCKRSM4F2VdJ6hPSwPIDoqKiKlVeq9USHR1dbrnw8PCqVqlC2Gw22SlKJH6E7Ksk9Qm5RFjHyc3N5Y477iAoKIiYmBjmzp1bqkzJafd///2XPn36YDKZaN++PStWrEBRFL7//nvAc9o9OTmZ/v37AxAWFoaiKCQmJgKe08xr1qxBUZRSH3dZgJ9++onu3btjMplo0aIFM2bMwG63q+cVReGdd97hmmuuITAwkOeff97rM7/11lu0atUKk8lEo0aNuP7669VzS5cupU+fPjRo0ICIiAhGjBjBwYMH1fPuZ/vqq6+4/PLLMZvNXHLJJezbt49NmzaRkJBAUFAQQ4cO5fTp0+p1iYmJXHvttcyYMYOGDRsSEhLCfffdh9Vq9fnbWK1WJk+eTOPGjQkMDKRHjx6sWbNGPX/48GFGjhxJWFgYgYGBdOjQgZ9//tnn/SSSuozsq2Rf5W/IGaw6zhNPPMHq1av57rvviI6O5umnn2bLli106dLFa3mn08m1115Ls2bN+Ouvv8jOzuaxxx7zef+mTZuyaNEiRo8ezd69ewkJCcFsNpcq16tXL1JSUtS/9+zZw1VXXUXfvn0B+PXXX7ntttt4/fXXufzyyzl48CD33nsvANOmTVOvmzZtGrNnz+aVV15Bq9WW+p7NmzczYcIEPvnkE3r16sXZs2f5/fff1fO5ublMmjSJTp06kZuby9SpU7nuuuvYvn07Go3G43teffVVmjVrxl133cUtt9xCSEgIr732GgEBAdx4441MnTqVt99+W71m5cqVmEwmVq9eTXJyMnfeeSeRkZHMmjXLq+zuvPNOkpOT+eKLL4iNjeW7775j6NCh7Ny5k1atWvHQQw9htVpZu3YtgYGB7N69m6CgIJ+/hURSl5F9leyr/A4hqbNkZ2cLg8EgvvjiC/VYWlqaMJvN4pFHHlGPxcXFiVdeeUUIIcQvv/widDqdSElJUc8vX75cAOK7774TQgiRlJQkALFt2zYhhBCrV68WgEhPT/f4/n79+nl8j5szZ86Ili1bigcffFA9dvnll4sXXnjBo9wnn3wiYmJi1L8BMXHixDKfedGiRSIkJERkZWWVWc7NqVOnBCB27tzp8Wz//e9/1TKff/65AMTKlSvVY7NnzxZt2rRR/x47dqwIDw8Xubm56rG3335bBAUFCYfDIYTwlMeBAweEoiji+PHjHvW58sorxVNPPSWEEKJTp05i+vTpFXoOiaQuI/uq8pF9Vf1DzmDVYQ4ePIjVaqVnz57qsfDwcNq0aePzmr1799K0aVMPv4VLL7202upks9kYPXo0zZo147XXXlOPb9myhU2bNnmMoBwOBwUFBeTl5REQEABAQkJCmfcfNGgQcXFxtGjRgqFDhzJ06FCuu+469fqDBw/y7LPP8ueff3LmzBmcTicAR44coWPHjup9OnfurP6/UaNGAHTq1Mnj2KlTpzy+++KLL1a/B6Bnz57k5ORw9OhR4uLiPMpu3boVIQStW7f2OG6xWIiIiABgwoQJPPDAAyxbtoyBAwcyevRoj3pJJPUF2VfJvsofkQZWHUYIUaVrFEU5D7Vx8cADD3DkyBE2bdqETlfUvJxOJzNmzGDUqFGlrjGZTOr/y9tBFBwczNatW1mzZg3Lli1j6tSpTJ8+nU2bNtGgQQNGjhxJ06ZNef/994mNjcXpdNKxY8dS/gfFHVLd8ih5zN3hlYc3eTqdTrRaLVu2bCm1fOCeWh83bhxDhgxhyZIlLFu2jNmzZzN37lwefvjhCn2vRFJXkH2V7Kv8EenkXoe56KKL0Ov1/Pnnn+qx9PR09u3b5/Oatm3bcuTIEU6ePKke27RpU5nfYzAYANcorizmzZvHl19+yY8//qiOfNx069aNvXv3ctFFF5X6FPc3qAg6nY6BAwfy0ksvsWPHDpKTk1m1ahVpaWns2bOHKVOmcOWVV9KuXTvS09Mrde+y+Pvvv8nPz1f//vPPPwkKCqJJkyalynbt2hWHw8GpU6dKPW/xEXnTpk25//77+fbbb3nsscd4//33q62+EkltQfZVsq/yR+QMVh0mKCiIu+++myeeeIKIiAgaNWrEM888U2YnMGjQIFq2bMnYsWN56aWXyM7O5plnngG8j24A4uLiUBSFxYsXc9VVV2E2m0s5OK5YsYLJkyfz5ptvEhkZSWpqKgBms5nQ0FCmTp3KiBEjaNq0KTfccAMajYYdO3awc+dOnztwvLF48WIOHTpE3759CQsL4+eff8bpdNKmTRvCwsKIiIjgvffeIyYmhiNHjvDkk09W+N7lYbVaufvuu5kyZQqHDx9m2rRpjB8/3qu8W7duza233sodd9zB3Llz6dq1K2fOnGHVqlV06tSJq666iokTJzJs2DBat25Neno6q1atol27dtVWX4mktiD7KtlX+SNyBquO8/LLL9O3b1+uvvpqBg4cSJ8+fejevbvP8lqtlu+//56cnBwuueQSxo0bx5QpUwDP6e/iNG7cmBkzZvDkk0/SqFEjxo8fX6rMunXrcDgc3H///cT8fzv3y6owFMZx/HfNCwaLwTTGGFoFQVnQgZYZjcNuGCKY1wWj2IyatNiWDQbfwdregOALuDdcsNw/ci/jOq/fD5x2OJz04zk8h6dcvq4wDCVJ3W5X+/1ecRyrXq+r0WhoPp9/+A9wS7FY1Ha7VbvdluM4Wi6XWq/XqlarKhQK2mw2Op1OqtVqGo/Hms1mPzr/O51OR5ZlyXVdDQYD+b6vKIq+3L9arRQEgSaTiWzbVr/f1/F4VKVSkfT+yh6NRnIcR71eT7Zta7FYZHZfIE/IKrLq2by8/qY5jn/lcDio1WopSRKZpnnv6+TScDjU+Xy+zt8B8PfIqtvIqvygRfiEdrudDMOQZVlKkkRhGKrZbBJYAHKFrMIjo8B6QpfLRdPpVGmaqlQqyfO8T6cqA8A9kVV4ZLQIAQAAMsYndwAAgIxRYAEAAGSMAgsAACBjbx1XJaJJBWulAAAAAElFTkSuQmCC",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=600.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proposal, runNB = 2956, 13\n",
    "good_params = tb.check_peak_params(proposal, runNB, 'FastADC2_9raw', bunchPattern='sase3')\n",
    "good_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e4fcef-4188-47f4-ae8d-6f9656dbdc12",
   "metadata": {},
   "source": [
    "## Extracting peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137414e9-b2de-4fe9-8182-808790623af2",
   "metadata": {},
   "source": [
    "The integration parameters are either user-provided or automatically computed by a peak-finding algorithm in `get_digitizer_peaks()` and `check_peak_params()`. The bunch pattern, when provided, is used to determine the parameters or to check consistency with user-provided parameters, and to align the pulse ID. The minimum required inputs to extract peaks are:\n",
    "* the `bunchPattern` source ('sase3' if the device is looking at the FEL or 'scs_ppl' if the device is looking at the PP laser), leaving `integParams=None` to let the peak-finding algorithm operate.\n",
    "* or `integParams` dict including `pulseStart`, `pulseStop`, `baseStart`, `baseStop`, `period` and `npulses` keys.\n",
    "\n",
    "In most cases, automatic peak finding provides good integration parameters. If it fails, or if we want to define fixed parameters to consistently analyze a series of runs, it is necessary to provide the parameters via `integParams`.\n",
    "\n",
    "If both the bunch pattern and the integration parameters are provided, the `period` and `npulses` of the user-provided parameters (`integParams`) will be overriden (with a warning in case of mismatch), except if `pulseStart` is a list (e.g. case of irregular patterns).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4fe745-9541-4ec4-aae4-ce02d47f6179",
   "metadata": {},
   "source": [
    "Once the parameters are found, we can extract the peaks using `get_digitizer_peaks()`, with `bunchPattern='sase3'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5653df96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pulseStart': 6763, 'period': 96, 'pulseStop': 6770, 'baseStop': 6762, 'baseStart': 6757, 'npulses': 400}\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
       "Dimensions:          (trainId: 7898, sa3_pId: 400)\n",
       "Coordinates:\n",
       "  * trainId          (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
       "  * sa3_pId          (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
       "Data variables:\n",
       "    FastADC2_9peaks  (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-11ada5d0-16ab-4ab9-b745-eaea54fc4888' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11ada5d0-16ab-4ab9-b745-eaea54fc4888' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 7898</li><li><span class='xr-has-index'>sa3_pId</span>: 400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ae88621-700e-4a53-9d7a-7e51c71cd5c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ae88621-700e-4a53-9d7a-7e51c71cd5c6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1501374970 ... 1501382869</div><input id='attrs-a99be75e-03c8-446d-ba8b-7eda71544a40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a99be75e-03c8-446d-ba8b-7eda71544a40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ace4fb93-60f2-4cbf-8784-a61293ad6858' class='xr-var-data-in' type='checkbox'><label for='data-ace4fb93-60f2-4cbf-8784-a61293ad6858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1501374970, 1501374971, 1501374972, ..., 1501382867, 1501382868,\n",
       "       1501382869], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>772 776 780 784 ... 2360 2364 2368</div><input id='attrs-ee0c31ce-becf-4f0d-9070-35e0baa0e35b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee0c31ce-becf-4f0d-9070-35e0baa0e35b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9b3b7ba-49c1-427f-9416-5434c5f2e283' class='xr-var-data-in' type='checkbox'><label for='data-f9b3b7ba-49c1-427f-9416-5434c5f2e283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 772,  776,  780, ..., 2360, 2364, 2368], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad695993-23cc-48a6-ac21-502b8bbdfdf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad695993-23cc-48a6-ac21-502b8bbdfdf0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FastADC2_9peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.04e+04 -2.104e+04 ... -1.866e+03</div><input id='attrs-a4f53d3d-8320-4992-a966-48841c81c574' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4f53d3d-8320-4992-a966-48841c81c574' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcbc121e-6815-417b-87a5-0a9deb569e77' class='xr-var-data-in' type='checkbox'><label for='data-fcbc121e-6815-417b-87a5-0a9deb569e77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FastADC2_9peaks_pulseStart :</span></dt><dd>6763</dd><dt><span>FastADC2_9peaks_period :</span></dt><dd>96</dd><dt><span>FastADC2_9peaks_pulseStop :</span></dt><dd>6770</dd><dt><span>FastADC2_9peaks_baseStop :</span></dt><dd>6762</dd><dt><span>FastADC2_9peaks_baseStart :</span></dt><dd>6757</dd><dt><span>FastADC2_9peaks_npulses :</span></dt><dd>400</dd></dl></div><div class='xr-var-data'><pre>array([[-10396.5, -21040.5, -11618. , ...,  -2953. , -14307.5,  -9120. ],\n",
       "       [-14979.5,  -3093.5,  -8993. , ...,  -5679.5,  -4255. , -25531. ],\n",
       "       [-22282. ,  -2814. , -21852.5, ..., -14205.5,  -8499.5, -13072. ],\n",
       "       ...,\n",
       "       [  -607.5,     99. ,   -660. , ...,  -1162.5,  -1217. ,   -808. ],\n",
       "       [  -999.5,   -681. ,   -327. , ...,  -1800. ,   -935. ,   -660. ],\n",
       "       [ -1075. ,  -1200. ,   -358. , ...,   -753. ,   -864. ,  -1866.5]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ba304c6-9a0b-4528-883d-decf6c893ca5' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ba304c6-9a0b-4528-883d-decf6c893ca5' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>trainId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-334df19a-f86b-474c-830b-6429a11b69c5' class='xr-index-data-in' type='checkbox'/><label for='index-334df19a-f86b-474c-830b-6429a11b69c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1501374970, 1501374971, 1501374972, 1501374973, 1501374974, 1501374975,\n",
       "       1501374976, 1501374977, 1501374978, 1501374979,\n",
       "       ...\n",
       "       1501382860, 1501382861, 1501382862, 1501382863, 1501382864, 1501382865,\n",
       "       1501382866, 1501382867, 1501382868, 1501382869],\n",
       "      dtype=&#x27;uint64&#x27;, name=&#x27;trainId&#x27;, length=7898))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sa3_pId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-824bcae3-76a3-4813-8f1a-64db2ec5f862' class='xr-index-data-in' type='checkbox'/><label for='index-824bcae3-76a3-4813-8f1a-64db2ec5f862' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 772,  776,  780,  784,  788,  792,  796,  800,  804,  808,\n",
       "       ...\n",
       "       2332, 2336, 2340, 2344, 2348, 2352, 2356, 2360, 2364, 2368],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;sa3_pId&#x27;, length=400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0babc36-33ec-4da4-a5b9-63800910d6a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0babc36-33ec-4da4-a5b9-63800910d6a3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25MB\n",
       "Dimensions:          (trainId: 7898, sa3_pId: 400)\n",
       "Coordinates:\n",
       "  * trainId          (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
       "  * sa3_pId          (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
       "Data variables:\n",
       "    FastADC2_9peaks  (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', integParams=good_params, bunchPattern='sase3')\n",
    "peaks"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1dbeb1e3-0126-4307-bdde-6d541fecbdc7",
   "metadata": {},
   "source": [
    "We see here that `peaks` has a variable `FastADC2_9peaks` which has dimensions `['trainId', 'sa3_pId']`, which is exactly what we wanted: the raw traces were automatically transformed into vectors of length `sa3_pId`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab2e1fe",
   "metadata": {},
   "source": [
    "Note that we could also have ommitted the parameter `integParams` in `get_digitizer_peaks()` to force the automatic peak finding algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2089bc40-54bf-4dc6-b4b9-97ac6fc6ae82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pulseStart': 6763, 'period': 96, 'pulseStop': 6770, 'baseStop': 6762, 'baseStart': 6757, 'npulses': 400}\n"
     ]
    }
   ],
   "source": [
    "peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', bunchPattern='sase3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa9fca4",
   "metadata": {},
   "source": [
    "## If the peak-finding algorithm fails\n",
    "\n",
    "\n",
    "The best strategy is to inspect the raw trace with `get_dig_avg_trace()` or use `check_peak_params()` with `show_all=True` and determine the regions of integration manually. Once the integration parameter dictionnary is created, one can feed it to the `integParams` argument in `get_digitizer_peaks()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0d64a0",
   "metadata": {},
   "source": [
    "## Save / load processed peaks\n",
    "\n",
    "If we have found good integration parameters, it is worth saving the integrated peaks as processed data. This can be done by selecting `save=True` in `get_digitizer_peaks()`. The location can be chosen by `subdir`, by default it goes to the `usr/processed_runs` folder of the proposal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d9bd039-b025-4be5-86cd-3ca5c542801c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pulseStart': 6763, 'period': 96, 'pulseStop': 6770, 'baseStop': 6762, 'baseStart': 6757, 'npulses': 400}\n",
      "saved data into /gpfs/exfel/exp/SCS/202202/p002956/usr/processed_runs/r0013/r0013-digitizers-data.h5.\n"
     ]
    }
   ],
   "source": [
    "peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', integParams=good_params, bunchPattern='sase3',\n",
    "                              save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b80062-fcf9-4d20-a775-1da949f925eb",
   "metadata": {},
   "source": [
    "To load the processed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b378684c-527d-480b-909a-4b5e1ffaf734",
   "metadata": {},
   "outputs": [
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