{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "483f3857",
   "metadata": {},
   "source": [
    "# MATPOWER Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b261dd8",
   "metadata": {},
   "source": [
    "See also the API reference for `andes.interop.matpower`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8130b077",
   "metadata": {},
   "outputs": [],
   "source": [
    "import andes\n",
    "\n",
    "from andes.interop.matpower import (start_instance, \n",
    "    to_matpower, from_matpower)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f237712",
   "metadata": {},
   "source": [
    "## Create an Octave/MATLAB instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79e2b3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = start_instance()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b1c973",
   "metadata": {},
   "source": [
    "## Convert to MATPOWER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db4fe683",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = andes.system.example()  # load an ieee14 example case\n",
    "\n",
    "mpc = to_matpower(m, 'mpc', ss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba19368",
   "metadata": {},
   "source": [
    "Test if the power flow of mpc can be solved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f05a127",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: error caught while executing handle class delete method:\n",
      "'osqp_mex' undefined near line 40 column 13\n",
      "OSQP Error!\n",
      "\n",
      "MATPOWER Version 7.1, 08-Oct-2020 -- AC Power Flow (Newton)\n",
      "\n",
      "Newton's method power flow (power balance, polar) converged in 3 iterations.\n",
      "\n",
      "Converged in 0.01 seconds\n",
      "================================================================================\n",
      "|     System Summary                                                           |\n",
      "================================================================================\n",
      "\n",
      "How many?                How much?              P (MW)            Q (MVAr)\n",
      "---------------------    -------------------  -------------  -----------------\n",
      "Buses             14     Total Gen Capacity     400.0        -112.0 to 150.0\n",
      "Generators         5     On-line Capacity       400.0        -112.0 to 150.0\n",
      "Committed Gens     5     Generation (actual)    226.4              49.8\n",
      "Loads             11     Load                   223.7              95.4\n",
      "  Fixed           11       Fixed                223.7              95.4\n",
      "  Dispatchable     0       Dispatchable          -0.0 of -0.0      -0.0\n",
      "Shunts             2     Shunt (inj)             -0.0              35.3\n",
      "Branches          20     Losses (I^2 * Z)         2.73             13.52\n",
      "Transformers      20     Branch Charging (inj)     -               23.8\n",
      "Inter-ties         0     Total Inter-tie Flow     0.0               0.0\n",
      "Areas              1\n",
      "\n",
      "                          Minimum                      Maximum\n",
      "                 -------------------------  --------------------------------\n",
      "Voltage Magnitude   1.010 p.u. @ bus 3          1.030 p.u. @ bus 2\n",
      "Voltage Angle      -9.48 deg   @ bus 14         0.00 deg   @ bus 1\n",
      "P Losses (I^2*R)             -                  0.50 MW    @ line 1-2\n",
      "Q Losses (I^2*X)             -                  2.11 MVAr  @ line 8-7\n",
      "\n",
      "================================================================================\n",
      "|     Bus Data                                                                 |\n",
      "================================================================================\n",
      " Bus      Voltage          Generation             Load\n",
      "  #   Mag(pu) Ang(deg)   P (MW)   Q (MVAr)   P (MW)   Q (MVAr)\n",
      "----- ------- --------  --------  --------  --------  --------\n",
      "    1  1.030    0.000*    81.43    -21.62       -         -\n",
      "    2  1.030   -1.764     40.00     30.44     21.70     12.70\n",
      "    3  1.010   -3.537     40.00     12.60     50.00     25.00\n",
      "    4  1.011   -4.410       -         -       47.80     10.00\n",
      "    5  1.017   -3.843       -         -        7.60      1.60\n",
      "    6  1.030   -6.453     30.00     20.99     15.00      7.50\n",
      "    7  1.022   -4.885       -         -         -         -\n",
      "    8  1.030   -1.540     35.00      7.40       -         -\n",
      "    9  1.022   -7.246       -         -       29.50     16.60\n",
      "   10  1.016   -7.415       -         -        9.00      5.80\n",
      "   11  1.019   -7.080       -         -        3.50      1.80\n",
      "   12  1.017   -7.473       -         -        6.10      1.60\n",
      "   13  1.014   -7.721       -         -       13.50      5.80\n",
      "   14  1.016   -9.481       -         -       20.00      7.00\n",
      "                        --------  --------  --------  --------\n",
      "               Total:    226.43     49.80    223.70     95.40\n",
      "\n",
      "================================================================================\n",
      "|     Branch Data                                                              |\n",
      "================================================================================\n",
      "Brnch   From   To    From Bus Injection   To Bus Injection     Loss (I^2 * Z)\n",
      "  #     Bus    Bus    P (MW)   Q (MVAr)   P (MW)   Q (MVAr)   P (MW)   Q (MVAr)\n",
      "-----  -----  -----  --------  --------  --------  --------  --------  --------\n",
      "   1      1      2     50.10    -18.36    -49.60     14.29     0.503      1.53\n",
      "   2      1      5     31.33     -3.26    -30.83      0.17     0.500      2.06\n",
      "   3      2      3     17.79      4.11    -17.63     -8.00     0.158      0.67\n",
      "   4      2      4     28.02      0.46    -27.58     -2.69     0.433      1.31\n",
      "   5      2      5     22.09     -1.13    -21.83     -1.70     0.262      0.80\n",
      "   6      3      4      7.63     -4.40     -7.58      3.21     0.047      0.12\n",
      "   7      4      5    -25.97     -5.70     26.07      5.99     0.092      0.29\n",
      "   8      6     11      6.91      2.37     -6.86     -2.27     0.048      0.10\n",
      "   9      6     12      7.93      1.32     -7.86     -1.17     0.075      0.16\n",
      "  10      6     13     19.15      2.76    -18.92     -2.31     0.234      0.46\n",
      "  11      7      9     39.12      1.46    -39.12      0.15     0.000      1.61\n",
      "  12      9     10      5.67      5.40     -5.65     -5.35     0.019      0.05\n",
      "  13      9     14     13.17     -3.85    -12.94      4.34     0.229      0.49\n",
      "  14     10     11     -3.35     -0.45      3.36      0.47     0.009      0.02\n",
      "  15     12     13      1.76     -0.43     -1.75      0.44     0.007      0.01\n",
      "  16     13     14      7.17     -3.93     -7.06      4.16     0.111      0.23\n",
      "  17      4      7      4.12     -3.76     -4.12      3.83    -0.000      0.06\n",
      "  18      4      9      9.22     -1.06     -9.22      1.53     0.000      0.47\n",
      "  19      6      5    -18.99      7.03     18.99     -6.06     0.000      0.97\n",
      "  20      8      7     35.00      7.40    -35.00     -5.29     0.000      2.11\n",
      "                                                             --------  --------\n",
      "                                                    Total:     2.727     13.52\n"
     ]
    }
   ],
   "source": [
    "m.eval(\"runpf(mpc)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b25d8bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpc_sol = m.pull(\"mpc\")  # retrieve the solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094c359c",
   "metadata": {},
   "source": [
    "One can also save the case to a MATPOWER `.m` file from Octave/MATLAB. Comment\n",
    "in and run the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e462b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.eval(\"savecase('case14_andes.m', mpc)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fed4d4",
   "metadata": {},
   "source": [
    "## Convert from MATPOWER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c04649f",
   "metadata": {},
   "outputs": [],
   "source": [
    "system = from_matpower(m, 'mpc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21763249",
   "metadata": {},
   "source": [
    "`m.pull()` won't work if one has run OPF in Octave/MATLAB because Oct2Py does not support custom class objects created by MATPOWER. \n",
    "\n",
    "`from _matpower()` will read the individual fields to construct an `mpc` dict internally before creating a system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4d2b28",
   "metadata": {},
   "source": [
    "## Add dynamic data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1609d34",
   "metadata": {},
   "source": [
    "One can create an Excel file with dynamic data only and use the xlsx parser to load data into system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "595ba30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<andes.system.System at 0x7f8684043520>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from andes.io import xlsx\n",
    "\n",
    "xlsx.read(system, andes.get_case('ieee14/ieee14_dyn_only.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73f07f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ab20b9ff74e4992b8387957a2d1ce0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?%/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Fault 1>: Applying fault on Bus (idx=7) at t=1.0 sec.\n",
      "<Fault 1>: Clearing fault on Bus (idx=7) at t=1.03333 sec.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.setup()\n",
    "\n",
    "system.PFlow.run()\n",
    "\n",
    "system.TDS.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "585749f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x400 with 1 Axes>, <AxesSubplot:xlabel='Time [s]'>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.TDS.plt.plot(system.GENROU.omega)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f970a278",
   "metadata": {},
   "source": [
    "Since the data in `ieee14_dyn_only.xlsx` is stripped from `ieee14/ieee14_fault.xlsx`, the above results is identical to the simulation of the latter case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "baea445a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9bd8a3fe6be4c96a4eb95d52532743e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?%/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Fault 1>: Applying fault on Bus (idx=9) at t=1.0 sec.\n",
      "<Fault 1>: Clearing fault on Bus (idx=9) at t=1.1 sec.\n",
      "-> Single process finished in 0.7220 seconds.\n"
     ]
    }
   ],
   "source": [
    "ss = andes.run(andes.get_case(\"ieee14/ieee14_fault.xlsx\"), \n",
    "               routine='tds', verbose=30,\n",
    "               no_output=True, default_config=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b1c11b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HQ1NT04IGrrrd7rQBsWaMsyUzZtn0rqX5JDDz1d3dTV1d3aKPQVkqkogsAq01VhXvlrFbLdI1I4S4bE0f+2AuJmbq6OiYcyCquVCZOR5jupkGws7G7/fPmQC1tbUxODiYfP6FXHv6NWaK3efz0dTUlNEys2vXrkVJRMwEzmwJmZ4oFSJJRBZBTMe7ZiCeiEjXjBDicmXOJPH7/TQ2NuJ2u/H5fAQCAXw+X3Kl0bn09PTQ2tqa0TISDAaTq5RON9NxIGMF1GxdPy6Xi7vvvjvrdQYHBzOOu91uXC5XMvnq7OxMrk/S2tqakQB0dnYCZE10zDVGzDqm9vb2eXev+P3+ZPeTOTvJXEG2kCmtV3Y3glKqAhgeHh6moqJiSZ6j7UeH+MEjp/lFy0t4Yet+Gus38aFdVy3JcwkhRCHz+/3s3LmT6upqOjo6qK+vZ+fOnQQCAZqamhbc2pC6xLvZCtLU1JQ2xddctn36MuxwYYn3QCCA1jqZEHV3dyfjmd660tjYmBy0al7bHKdRV1eXtgS7OQuoubkZl8uVjMlc4n367J+5kgpzxo+5pL2ZoMwlGAyybdu2rONJcvmcHxkZobKyEqBSaz1y0ReahSQii6D1vif58eNn+PlHXsKL23/Gq5+/Dt8rrlmS5xJCCCEulUuRiEjXzCIwEuuIANitSrpmhBBCiHmSRGQRxIz4XjNgDlZd2a1MQgghxGJZ0DoiSikXsBto1FrPaycdpVQLEEx869Jaty+kfFrdnvk+76VkTJs1E5YWESGEEGJe5t0iopTyEE9CXMC8huAmkgy01p1a607Ar5TqmG/5tGs1ALPP+cqT1K4Zm3TNCCGEEPM270REa+1PJAsLmZC8F0jORdJa9wJNCygHki0xBTv/KGbEN70D6ZoRQgghFmLJxogopdzEu1qCWcq8c5VPO7QbuGeez+tUSlWYD6B8wcEvkKHBmshEHNI1I4QQQszbUg5WnWnic5B4985c5UAyKclc63dme4HhlMeJ2avnzjAuLGgmXTNCCCHE/OVj1swgs3ezTC93aa0X0h3UClSmPDYuOMIFimmNVbpmhBBCiAXLRyIy11iPZLlSqklrnX3B/hlorUNa6xHzAYxeTJALYWid1jWTuteM1prY6JKHIIQQQixLS5mIzNSK4UqUzVqemKUz81aJBWR614yZiJz51N9x6PnXc/pv/zaf4QkhhBAFa0HriCyE1jqglAoqpdzTu1YSs2OYrTwxNsSTMnC1LnFOCxBYaEvJUjI003bf1WitCR8+TOWrX41r9/y3lRZCCHFB6l4zEN+YrqmpCZ/Pl9y3xtwPxtzgLdteM263m56enoy60ze/8/l8dHZ2Ul9fn9yHJts+NgMDAwSDwYzN9EzmXjPmJn/mhnnT95oJBAJ0d3fT2tqajD11LxszHq/Xy969ezN27V0RtNYLehCfXnswy3E30DLtWAvQlPJ9A9Ax3/Jp1/LEw11wvBWAHh4e1kvlT79xUP/x3Q9orbX+y289ol/7hV8u2XMJIcTl4ODBg9rj8eiDBw+mHe/r69MNDQ3a4/FknOPxeHRTU1PG8aGhIe31etOOtbW1aZfLpVtaWjLqZzvm9Xozrt3X16ddLldGjAcPHtRer1cPDQ2lHe/q6sqIY67YZ4onm6GhId3R0aE7Ojp0S0uLbmhoyIhhoYaHhzWggQq9wM/f+T4WsqCZO9Ea0Uy8paItsciYyZsoS01y2gGXUqohUXeH1rp5vuUpz91AfDYMiectqIXNtNYpS7yreIuIYcTHh4yNMXTPPUTOnMlzlEIIsXzs3LmTtra2jBaA6bvrppppu3uXy8WuXbsyjnV1ddHe3k4gkD5SYPqOuTNxu93U19ezZ8+ejNh9Pl/Grr7mTrrZ4p8p9oXE4/P58Hq9yV2Fq6uraWws/Bb5hSxoFtBat2utt2utldbap1O6R3R8ddSMn1binO7Ew7fQ8kSdbq11Y8rzLmQ675KLGRcGq9oTg1VHfvADnq7fgTE8zJmPfZzQU0/lOUohhFgempubcbvdeL3Z/+b0er2zfnCbgsFgMsnweDwEg8GM63i93pw+rF0uV9p1za6amWI3u1qmJz+LwezmMdXV1XHgQOEPtZRN7xbB9E3vojGDomuvpfYDH8C2fj3XPPIHym67Lc9RCiHEpTP9Q98UCATo7Z39b8ne3l7q6+tnrWOOHZlNIBBIxuH1ejNaKAC6urrw+/10dnZmlM0lGAzS29ubHKsC0N3dnXXMiMksS00YFktPTw8tLS3J7x966KEZE6JCsmSDVS8n2bpmnHV1OM3mNLs9j9EJIZab/ol++if7Z62zrnQdVUVVDE0NcXr8NNfWXAvA4eHDTEYnZz33SteV2K12jo8ex9AGWyq2EDNiPDV0oeW2triW2pLai4q/vb097QMxlfnBPX2QaKpAIDBnd8RMH7AHDhygvb2dgYEBuru76erqmvU6LpeLtrY2fD4fu3fvzpqspMZlJhCBQIC+vj7279+f1n0UCARm7Doyud1uHnrooVnr5Kq7u5tgMDjn/RcCSUQWQUxrHJb03XcnHnoIS3k5Rddcw7l/+icAVn/wg/kLUgixbHQ93cWX/vClWet8/IUf501XvYmfHvspH3/g4zz69kcB+OivP8oj/Y/Mem5PQw9rS9fy+YOfZzwyTseuDiajk9zx/TuSdd53/fv40xv+dMGxB4PBWWd2NDU10dPTs+Drzld9ff2MSdBMWlpa6OjoYM+ePbN+cLvdbhoa4kMj/X4/+/bty9qtMzAwsLCgF1EwGOSee+4hGAzS2Ng4a2JVKCQRWQSpe82YXTNnP/sPOK+6kvV/93dYyyvyHKEQYjlpvKqR2zfdPmuddaXrAHjp5pfynJrnJI//3S1/N2eLSE1RfCrsX2z/CwwdX/eo2FbMvtfsS9apLb641pDe3t7kh7X5PaS3YEwfODqdx+PJ2mLS29uL3++no6ODwcFB9u7dO2vS0dzcnNZF5Pf7Z0ySurq62L59O36/f9bYUmPcu3cvjY2NDA0NJY+73e45x3/Mp9Uk1UzdXNmY05sBOjs7qaqq4vDhwwWdkEgisggMQ2OxpHfNbP7qVyEWBaDm3e/KZ3hCiGWmtmT+3SJVRVVUFVUlv99WuW3ez7OpfFPya6vFmuzeWUw9PT1pH7qBQGDWMRQQT1qyjaEwB5ea3S5ztXxMf54DBw7MmIh4PB6amppobGyc1/gT8/rmgFjzubxe76xjYMxEZ3rX0nwSmNmY65bs3bs3mXR4vd7kOJbU5LDQyGDVRRAzdNpg1XDMwFpWirWyEgAdiRBNLGYjhBAr2fSxD+ZiYqaOjo45B1Cagz9nGtAZDAYX/Be+3++fMwFqa2tjcHAwbfDpfK89/Rozxe7z+WhqaspIiHbt2pVTIhIIBGhvb08unAYXWlIKuTUEJBFZFEbKpnc2q8KIRjn54buY8D8MwFBXF8/cdru5wJoQQqxY5l/gfr+fxsZG3G43Pp+PQCCAz+dLrjQ6l56eHlpbWzNaF4LBYNqHbaqZjgMZK6Bm6/pxuVzcfffdWa8zODiYcdztduNyuZLJV2dnZ3J9ktbW1ozEwpyZky3RMdcYmT57p729PWM11mw8Hg8tLS1p97hv3z48Hk/Bz5yRrplFYOj0FhFbNEKkvx8dmgKg7NZbsa9dC1pDop4QQqxEzc3N7Ny5k+rqajo6Oqivr2fnzp10dnbS1NQ07y4Ct9vNwYMH8fl89PT0UFNTk/zLvqurKy1BMZdt9/v9BINB2tvbk2XmEu9m94mZEJmzSsxl3E0NDQ3s27cv49rmdGIzMXC5XMmko62tLW3artfrZf/+/bS2tmbM/pltoG5PTw/t7e34fL7kkvYNDQ3zbtHYu3dv2r0Hg0H2798/r3PzSa30v9KVUhXA8PDwMBUVSzNo9E1f+g3bVpXyD43X852HT/LBfb/n0KdeQZHduiTPJ4QQQlwKIyMjVMaHGVTq+I72i066ZhZBzNDJTe9sVgVaJ3fgBYgNDzO07x6i/bOvCyCEEEJcbiQRWQSG1lgSP0m71cKOs09y4uabkolHbGSEMx//OKHA4TxGKYQQQhQeGSOyCFLHiDisFo6Xr6Hkz96PJTFrxr5xI9c8/hjKInmfEEIIkUoSkUWQuteMzao4U1qDtfElWBwOAJRSMkhVCCGEyEL+RF8EWqfvvrt55AyhgwfT6pz88F0Ev/OdPEQnhBBCFC5JRBbB9AXNXn70QcKfbU2rY62sSLaQCCGEECJOumYWQXyMSPxru1Xx9ee8nD96xw1pddb+7d9e+sCEEEKIAictIotg+qZ3UzYnkcrq9DrhMLGx8XyEJ4QQQhQsSUQWQczQ8QGpxBORNz3zM/T37k2rc/IvPsTJD38oH+EJIYQQBUu6ZhaBoTXW5DoiitUTQ+j+c2l1at79LjCMLGcLIYQQly9JRBaBMW2w6peufyMveMtNaXVKZth6WgghxMx8Ph9Acu8Vl8tFU1MTPp8vuXmcuR9MZ2cn1dXVNDc3J88395pxu9309PRk1J2++Z3P56Ozs5P6+vrkPjQdHR20t7fjdruT1x4YGCAYDGZspmcKBoO0trYmN/kzN8ybvoFdIBCgu7ub1tbWZOzmXjap8Xi9Xvbu3Zuxa++KoLVe0Q+gAtDDw8N6qdz89736cz95Smut9bmRKb3F933d8/iZtDqTTz6pg9/7/pLFIIQQK8nBgwe1x+PRBw8eTDve19enGxoatMfjyTjH4/HopqamjONDQ0Pa6/WmHWtra9Mul0u3tLRk1M92zOv1Zly7r69Pu1yujBgPHjyovV6vHhoaSjve1dWVEcdcsc8Uz3zM9FwLMTw8rAENVOgl+pyWMSKLIJa2jojiP3/8dxR/rzutztj9v+Dspz+dj/CEEGLZ2blzJ21tbRktAKmtEtNVV1dnPe5yudi1a1fGsa6uLtrb2wkEAmll03fMnYnb7aa+vp49e/ZkxO7z+TJ2zW1oaJgx/pliX0g8qbq7u9N2KC5kkogsgnjXTPxru9VC95W3M3bFc9Lq1Lz7XVz5q1/mITohhFhempubcbvdeL3erOVer3fWD25TMBhMJhkej4dgMJhxHa/XS2Nj40XH6nK50q5rdtXMFLvZ1TI9+VlMwWAw2RW0HEgisgjim95dWOL9e+4XMbb1qrQ6ymaTvWaEEJeN6R/6pkAgMOdf6r29vdTX189axxw7MptAIJCMw+v1ZrRQAHR1deH3++ns7JzzetMFg0F6e3uTY1Ug3hKRbcyIySzr7u6esU6u7rnnHnbv3r1k119s8sm4CFJXVrVGInjOPYUeCabVmXz0MY41NREbHs5DhEKI5SRy7hxTTz2d/D707LNETp8GwAiFmHz88eS6RNHz55k6dOhC3cBhIidPAqAjkXjd0dF43cFBpp54Ilk3fOQI4RMn4nVjsXjdRfg3qr29PeuHPsSTjJm6VkyBQGDO7oiZWhwOHDhAe3s7Pp9vXi0dLpeLtrY2fD7fjMlTalzd3d10d3cnn2P//v00NDSk1TEHqM7E7Xbz0EMPzRnbxejt7Z3xZ1OoJBFZBFqDNZGIGP3n+Pvf3I3tcPpIbGWzYnE60dFoPkIUQiwjwX33cDxldsXJD32YgX/7KgDRM2c48qYGph5/HIDh736Xo29/R7Lu6b176f/Sl+J1h4Y48qYGJhJ7X4388IccuePNF+p+4hP0f+7zABiTkxx5UwPjDzyQW+zB4KwzO5qampZ05kd9fT0tLS20tbWlJQizaWlpobq6OmOsx3Rut5uGhgYaGhrwer0cOHAgaxfIwMDARcW+GILB4KwtMoVIpu8ugpjW6DOTdH7wfm7cuZEPveqjvHvr1Wl1ip7zHDZ+4Qt5ilAIsZy47thN+ctelvx+w+f+EUtpKQC2tWvZ+q1uHFu2AlD5+tdTesstybrrWluxOOP7WtmqquJ1N28GoOKVr6Tkxhsv1P3Yx8AW/xiwFBfH627cmFPsvb29aQmA2Q2T+lf69IGj03k8noxptea1/H4/HR0dDA4OsnfvXlpaWma8TnNzc1orh9/vnzEJ6urqYvv27fj9/lljS41x7969NDY2MjQ0lDzudrvnHP8RCATmbBVKNVdLjamzszNjevByIC0iiyBmaIy+MSJTMY48NkiwYhVhmz2jng6HpUVECDEn++rVFF19YZyZ84orsK9bB4DF6aT4uuuwliUSk1WrKLrmmgt13duwb9gAgLLb43XLy+N1q6spuvbaZF3H1q3JxENZrfG6lZWLei89PT1pf6EHAoE5/2L3er1Zx5F4vV5aWlpoaGigurp61iQE4klBauJx4MCBGet6PB6ampoWNHDV7XanDYg1Y5wtmTHLpnefzCeBmY3f759zXE2hkkRkEWgNeiKeYJw/MUrTwW9jTNtXJnLuHIeefz1jv/pVPkIUQohLYvrYB3MxMVNHR8ecYxjMwZ8zDegMBoMzjkGZid/vnzMBamtrY3BwMG3w6XyvPf0aM8Xu8/mydk/t2rUrp0RkcHCQ3t5e2tvbk+NXID5eZykHxi4GSUQWQUxr9HiUzdfVYMRg4+QkZycPp9WxuVys+0wrRVdfPcNVhBBi+TNnkvj9fhobG3G73fh8PgKBAD6fb86BnKaenh5aW1szWkZmm5o625TV6SugZuv6cblc3H333VmvMzg4mHHc7XbjcrmSyVdnZ2dyfZLW1taMxMKcmZMt0THXGJk+e6e9vX1e3S1ma5H5MLt+zBakQqZ0fPXRFUspVQEMDw8PU1FRsSTPUfeXP+BDwWJue8tV/GLfM/xywy94YuO93Pv6b+N2La9BQ0IIkQu/38/OnTuprq6mo6OD+vp6du7cSSAQoKmpacGtDalLvJutIE1NTWmzQ8xl26cvww4XlngPBAJorZMJUXd3dzKe6a0rjY2NdHV1pV3bHKdRV1eXtgS7OX23ubkZl8uVjMlc4n367J+5kor29nYGBgaSS9qbCcpCdHd3s2/fPrq7u2lpaWHXrl0XPZNmZGSEynh3XaXWeuSiLjIHSURypLXmhpYf0jRaxOv+/AZ+8vVH+G1xDwe29uDdchufve2zybojP/4Jzjo3ziuuWPQ4hBBCiMV2KRIR6ZrJkaGhXMen7pZVO1FTZ3nRE9Vsc7yUh889nFb3zMc+xuj+n+YjTCGEEKIgyfTdHMUMTbkRT0RKXU5c6ys4PrEJl9XFMxM/4NzEOVaXrAbgip/uRzmd+QxXCCGEKCjSIpIjQ2usid4tm8PK1uuvBesqyo14n96j/Y8m61pKSlBWaz7CFEIIIQqSJCI5MlK2MDZ0jKfO/AKbAfZwJW+99q1UF1/YmOnc5/+JwW98Iz+BCiGEEAVoQV0zSikXsBto1FrPvjTehXNagGDiW5fWun2+5SnPB1AHuIE9WusgBSJmJBIRCwTDQYbu+y9w+WAyRsuO9MV29NQUOhzJS5xCCCFEIZp3i4hSykM8KXABc++/TDLJQGvdqbXuBPxKqY75lgNtQG+i3AcMAl3zjflSMDQoQCnFquJVvPnT/waAmohxZvxM2oDVNXv/kpp3viM/gQohhBAFaN6JiNban0gWFrL0214guTqL1roXaFpAuRtIXYmlDyioNWwNQ2PRgCU+YLVq6wYMBZapGF1Pd3HX/Xel1deGkYcohRBCiMK0ZGNElFJu4l0twSxl3rnKAbTWu6Z15ewAMjcgyKOY1lhQKAv86MiP+Je/fiVKhbGFDN5yzVv45qu+max76qMf5eif3JnHaIUQQojCspTTd2daCi5IvHtnrvI0SqmGxPFZdyRSSjmB1Dmy5bPVz5U5WFUpxeHgYcr7zjJZNY5tqphVxavS6rpe/3pio6NLGY4QQgixrORj1swgs48xSStXSrmUUk3EE5eueQxU3QsMpzxO5BTtHAwj8UO0KPon+/nBW68kuHYN9rDBcGiYj9z/ER47/xgAJTt2UP7Sly5lOEIIIcSyko9EZK6BrmnlWutgYrBqO4BSaigxm2YmrUBlymNjDrHOydAapUFZoH+yn1XFq9DFVpwRTZGtiB8d+RF9wfjmSuEjRxj96c+WMhwhhBBiWVnKrpmZBrW6EmWzlieSjb1Aa0orSG+i3Atk3ddYax0CQub3SqkFBb1Q5vRdZVGcnzjP+//pMI8/72YmI5twWByUO8rpn+wHYHT/Tzn/5S9z9UMPLmlMQgixUqRuegfxHXKbmprw+XzJDfTMjek6Ozuprq7Ouumd2+2mp6cno+70XXh9Ph+dnZ3U19cnN8TLtqHewMAAwWAwY1dfk7npnbnbsLlz7/RN7wKBAN3d3bS2tiZjT91Uz4zH6/Wyd+9ePB5Prj/SwqO1XtCD+CyWg/OsOwS4px3T8ykHPIBOLU855l1AvBWAHh4e1kvhyPkx/dY//5Hu9P1Sv/Jbr9T3/s1b9Rc/+R39xeb9emIkpF937+t06+9atdZax8bHdXRkZEniEEKIleTgwYPa4/HogwcPph3v6+vTDQ0N2uPxZJzj8Xh0U1NTxvGhoSHt9XrTjrW1tWmXy6VbWloy6mc75vV6M67d19enXS5XRowHDx7UXq9XDw0NpR3v6urKiGOu2GeKJ5uenh7d0NCgOzo6dE9Pj25padFdXV3zOncmw8PDOvG5W6EXmC/M93ExXTNZu1aUUm5zXZAUrcRbL8w6DaRM152tXGvtB9q11qktJ3cAfh2f5lsQDE2yRWQsPMb5xhczVrcVgLGhELXFtfRPxFtELCUlWMuXdOysEEKsCDt37qStrS2jBSC1VWK66ursPf8ul4tdu3ZlHOvq6qK9vZ1AIL2Bvq6ubl4xut1u6uvr2bNnT0bsPp8v2aphamhomDH+mWJfSDzBYJDe3l6am5tpbm6mrq6OhoaGuU/Ms4UsaGYmGs2ARynVlkgcTN5EWZKOj+twKaUaEnV3aK2b51sOtCqlWswH8W6ZnQu9yaUUMzRKKywWxfjUCLWHh3FYwgCMBUOsKlnF+cnzAEw+9jgnP3wXsbHxfIYshBAFrbm5GbfbjdfrzVru9Xpn/eA2BYPBZJLh8XgIBoMZ1/F6vTQ2zjoZc1YulyvtumZXzUyxm10t05OfxXL48GG01vT19WV0AxWqeY8RSbRMtCce2co7SW/tMI+n1s8Y1zFbuY6PDcn6fIUi2axk0Tgno1zV8hXG3rOKATYyNjhFbVktj/Q/Eq8bCRPt70dHwkBpPsMWQhSoSDhG8MxEvsMAwLW2BLvj4jbqDAaDGS0CEB8TEQgEZvygBujt7Z21HC6MHZlN6of9TNfr6uqiqqqKzs7OBX9wmy0Qd999d/JYd3f3rOM4zPEk3d3dtLRM70S4PC3lYNXLQiyRiFgsFr5/58+w3nqKXxzVTBweiLeI1F5oESm58Ua2fO0/8xuwEKKgBc9McM+nH8p3GADs/qsd1G5eeHdye3v7jB+yvb29tLW1ZQwSTRUIBObsjpgpsThw4ADt7e0MDAzQ3d1NV9fsu4K4XC7a2trw+Xzs3r07a/KUGld3d3fy676+Pvbv35+WeAQCgRm7jkxut5uHHlqa1/iee+6hurqawcFB+vr6kgN6C5kkIjky1xFRFkV1eS08txbLYIAxy3nGhqZYVbyKyegkE5EJSuwl+Q5XCFHgXGtL2P1XO/IdBhCPZaGCweCsLQJNTU309PTkEtas6uvrF9zS0NLSQkdHB3v27Jk1cXG73ckxF36/n3379mXt1hkYGFhY0IvE/LmbrS6dnZ00NjbOmYzlmyQiOTJ0fK+ZiA7h+9Z7eO+zWyi57pWMKs14MMzt62+h+7XdOKwOwkeOEHj9H7H5q/9Gyfbt+Q5dCFGA7A7rRbVCFIre3t60AZK9vfG5BaktGNMHjk7n8Xiytpj09vbi9/vp6OhgcHCQvXv3zpp0NDc3p43f8Pv9MyZJXV1dbN++Hb/fP2tsqTHu3buXxsZGhoaGksfdbvec4z/m02qSavrYlplMn0a8e/fu5M9gtpaefMvHgmYrSszQ8d13LVA8HiX8k59RFJlkVGkmhkO4ilxcXX01NosNa00Nq++6C/vGJV1jTQghCkZPT0/aB2QgEMi67kYqr9ebTGCmH29paaGhoYHq6uo5Wz7cbnda4nHgwIEZ63o8HpqamhY0cNXtdqcNiDVjnC2ZMcumdy3NJ4GZi9ltZDKTj6UaGLtYJBHJkbnXTLGjhI+/7T+4ev9PiW3aGm8RGQ4zFh7jkw98kicHnsRaXk71W+/EvmZNvsMWQoglMX3sg7mYmKmjo2POgajmuIbpH6ymi/kL3+/3z5kAtbW1MTg4uOBxFamJh3mNmWL3+Xw0NTVltMzs2rUrp4QhGAzS2NiYdg2zJWWu+843SURyZCR239XKYHAqvnKew2phzKIJT0Yhqnjs/GMEQ0G01oz98pdETp3Kc9RCCLE0zJkkfr+fxsZG3G43Pp+PQCCAz+dLrjQ6l56eHlpbWzNaRoLBYHKV0ulmOg5krICarevH5XJx9913Z73O4OBgxnG3243L5UomX52dncn1SVpbWzMSi87O+MTSbImOucaIWcfU3t4+r9k8LpeLlpaWtHvs7OykoaGhoLtlAJSOrz66YimlKoDh4eFhKioqFv36Dx0Z5Bv/eJBtqyd5nE/h+00tT33s//GZrme4Y9zJnZ96AZW18QFfWmsOXfdc1v7t31D15jcveixCCJFvfr+fnTt3Ul1dTUdHB/X19ezcuZNAIEBTU9OCWxtSl3g3P1CbmprSpviay7ZPX4YdLizxHggE0FonE6Lu7u5kPNM/qFMHeJrXNlsX6urq0pZgN2cBNTc343K5kjGZS7xPn/0zV1Jhzvgxl7Q3E5T5CAaDaYnMwMBAzrNmRkZGqKysBKjUWo/kdLEZSCKSo98GBtj3OT+b1o7ymOuLtI6/gj+8/M385Tce492jRbzhwx7WXVFJVEexW+xEzpzB6nJhKSpa9FiEEEKIxXQpEhHpmsmRkdj0LkaEkc3VrPG1YC8uYswST/DGh0O89Ydv5e9/+/cA2NeulSRECCGESJBEJEfmXjMxYtRM2Qj1fpWi2ARhwGq3MDEcpsxRRjAUBODcP/8zI/fdl8+QhRBCiIIhiUiOzJVVDRXF88AZjnyojcrg46DAWW5nPBiiylnF0FR8nnnk6DGi58/nN2ghhBCiQMiCZjkytEbpeItIoG6ct1XV8uSGFwIP4CizMz4conJTJYcGDwGw4XP/mN+AhRBCiAIiiUiOUseIxBwTFL/wldSc/CmlgL3MzvhwmApHBSOhJRnjI4QQQixr0jWTo/gYEUWUKO6nQwzd/yRbfvJurlbHsZXZmBgOxRORcDwROdnSwokPfDC/QQshhBAFQlpEchRLtIiUOIq58lwJo+EwVddAneUU1mIb48EQa5wVTMWmCMfCVLzsZeiYke+whRBCiIIgiUiOzE3v6te/EO9H3glA9B+vwz10GkupjfBUjDLiG1iNhEdYNcfSxkIIIcTlRLpmcmRoc9M7DYnF4WLVV+BWp1HFVgBKIy42lG1gKjpF+MQJJh5+OI8RCyGEEIVDEpEcmV0z9z77P/y08WrOfuYzGOu3M0YRuiieiGyybuVHb/oRG8s3Eryni1N3fSS/QQshhBAFQhKRHOnEgmY7impZVwmOujqit+3lw5E/RRfHf7zjw+Fk/ep3voMt//X1PEUrhBBCFBZJRHIUM+JjRK6y2HnO9lVUNTZit1qwYBBVBjaHhZGhSW7fdzv3Be7DVlWFfd26fIcthBBCFARJRHIUS4wReSoywNGQnVgwiP38kzzlfDvlQ09SWukkNBLlT57zJ9S56ph4+GHOfPKTrPTNBoUQQoj5kEQkRzqxxPvPYucZ7h7k/Je+jLViHXYVwzF+ipJKB+PDIfY8fw9XV19NbHCQid//HmKxfIcuhBBC5J1M381RzCCx14zBmO9dXPP8N0BJNVPaTtHEKUpdNzE+HOLg2YMU2Yq4budOynfuzHfYQgghREGQFpEcGclN7wysO/4Pjq1bQSlOq1UUT5ymtNLJxHCYf/b/M9988pv5DlcIIYQoKJKI5MhMRLQyKLm7g8lHHwXgLLWUTJ6Od80EQ8n9ZiZ//3uevvVWwseP5zdwIYQQogBIIpIjc9aMoWLY7vMz+cgjAPy9/f/yk7q/prTSSXgqRqXFxUh4BNuaNVT/8R9jKS3Nc+RCCCFE/skYkRwZGhQKQxkUf/IVVL/sTwAYstUypkopdTkBqIjVMBJ+HPu6dax63/vyGbIQQghRMKRFJEexmIEFhcagyFGWPH49z/CGJz+E2fBRFnYxEhrBCIeZePhhYsPDeYpYCCGEKBySiOTIMOLrgVh0jLH2HsZ+/WsAyixhrhz+NaWWAQCKw+WMhEeIBYMcfcsfM+H35y1mIYQQolBI10yOYolE5Lbi9RRdZcNaHt9pd8heC4B96iQ2hwVnqJSp2BSxylK2/e93cWzYkLeYhRBCiEIhiUiuYvFEZNcL38/GP1+TPDxsXw2AGjlJaeU2rJMGKBiNTVB71VV5CVUIIYQoNNI1k6NYIhF5uu+HRI4+jQ7HN7jTtmJGrS4YPkGpy4lzqow7n3MnNouNc5//JyYOHMhj1EIIIURhkEQkR7GYAUD30z/m2Ze/nsk//AEAh9XCd1e9B9y3U1LpQE3Y8N3ko6qoirFf/ILw8RP5DFsIIYQoCNI1kyOdGCPy/vHzbPz7D+O88koA7FbFLxyv5M5N9ZRWPsP546P8of8PbCzbiPveb+czZCGEEKJgSItIjsxZMzWWCOW3vgirywWA3WqhdjIAj30rsfFdmDvvu5PfnPpNHqMVQgghCoskIjkyEmNE7nGUMvi/+4mNjgJgt1l47sTv4HsfpLTSSWQqxj2v6OIlm17C8eb30v+FL+YzbCGEEKIgSCKSIzMROROxc+6fO5ILlTmsFs6pGgiNUFocBWCd2kyZo4zSF72IouuuzVvMQgghRKFY0BgRpZQL2A00aq13zfOcFiCY+NaltW6/iHKAOgCtdfNCYl5qZtfMqW01XPPod5LH7VbFWVYBUGIdBOAbB/+HzVfV8tq33nnJ4xRCCCEK0bwTEaWUB6gHXED1PM9pAdBadya+9yqlOsxkYh7lbVprX8r1OpRSPfNNgi4Fc7CqxarSjtutFk5RA0CpPgdYeOp4gP7KY7yiyIOORHBs3XqJoxVCCCEKy7y7ZrTW/kTCEFjA9fcCnSnX6AWa5lOeaH3xJP5v6gC8Sin3AmJYUmaLyJanT3L0ne/ESKwjYrdaOGtUwaYX4Ci2YrNbKI9WMRIe4WxbO2c+9Xf5DFsIIYQoCEs2fTeRLLi01sEsZV7iCc1s5QeIt8C4AXNjFjMJci1+xBfHbBExLBFsVVUoqxWId81MGRZ4949RQEnlbyiJVHAufIjVH/5oHiMWQgghCsdSriMyU6tFkHgiMWt5IkGpmlbmTfx/xlYZpZQTcKYcKp89zNyYg1XPrtVs+PPPJY/brRbCMQOMGITHKHU5KQqVMRoexbFly1KGJIQQQiwb+Zg1M8jsY0xmK98LNGdrRZlWZzjlsaRLmJotIvZYDGN8PHncbrUQiRnQ/S7Y91ZKKpw4pkoYDY8y9utfM/Af/7GUYQkhhBDLQj4SkbkGumYtV0q1AfvMga2zaAUqUx4bFxzhAuj4Cu9c/4Tm2ZfuTB532CxEYxoq1sPISUpdDqyTTkbDo0w98QSjvb1LGZYQQgixLCxl18xM3SeuRNlc5UlKqQagbx5JCFrrEBBKOXceoV48c7Dqi17zTta9eVPyuN2q4l0zFRtg5BSlVzpgwsZoeJSa97yHVXv2LGlcQgghxHKwZImI1jqglAoqpdxa68C0sl6AucoTdbyJY+YUXxdQPf2cfDG7Zty3vpny6qLk8WTXTOUGiExQUhxFhyzoqGIyOkmJvSRfIQshhBAF42K6ZmbqOnGnLD5mauXCAFOzZaNzvuWJtUs8gD9xfTfx6b2DFxH3kjATkZ//1/sZ3b8/eTyeiGioiPcMldriK65+4QVfIrT/5/S9+jVorS99wEIIIUQBmXcikpJoNBNf36MtkTiYvImypMQqqS6lVEOi7o7UlVFnK0+0fOwH2oC+lEfbHINVLykzEdEPPEb///tC8rjDaiFmaGJrnw++I5RsvRqAKxxXU7R+I2W33gqGkZeYhRBCiEIx766ZRFdIe+KRrbyT9NYO83hq/e75ls8wfbfgmK0au15UQVHzhduz2+JjUyLYsBZXUeqKAHDPw/fyhpfvZPNf+jIvJoQQQlxmZNO7HOnEOiJWux1lu5DX2a3xH20kZsD/vh/nY/+BxaY4dOJZ+oMnCQUC6MQqrEIIIcTlShKRXCWGedzrP8e5z/9T8vCFRETD+WdQJx+itNLJ27a+i2vOWAm86tVETp3KQ8BCCCFE4ZBEJEfmGJFYcQVYLkwVdqS2iFRsgOETlFY6mRgOUfSc57Dlm9/AtnZtXmIWQgghCoUkIrlKjDc9vPslrP7AB5KHzRaRcNSAqi0QPEZppYNfP/Mg/3HkHko8HixFRdmuKIQQQlw2JBHJkTlY1T41ho7Fksft1sRg1ZgBVVth+AQl5TYcoRLGhs5x/ssdhI8dy0fIQgghRMGQRCRH2tAYKsYLP/tDTn74ruRxW+oYkW0vhjd0UFpppyhUxsTEMINf+xrh48fzFbYQQghREJZyiffLgwGaGEdfWIHnZW9JHk4bI1K1Faq2UjJ+GnukiGP2KFf95td5ClgIIYQoHNIikqN4i4jB6JXllN58U/K4uY5IOJYYROL/OqWRwwBMjUYueZxCCCFEIZJEJFdao5XBqr4pJh97PHk4OX03mkhEfvU5Ss/+PH5sVHPkLX/M8A9+cKmjFUIIIQqKJCK50qCVgbt3kOC3LqysanbNRBPTe6naSsnUswDExhRFz30utpqaSx6uEEIIUUhkjEiOtKGxWsq58YcPpB1PTt81u2aqtlF07Hdg0TBhY+1f/9WlDlUIIYQoONIikisNKIWlqChtXZDk9F2za6ZqKyp4BEupQXG4gsi5c0SHhvIQsBBCCFE4JBHJldaEdT8P7nkFI/fdlzxst6VM3wVYfyNc9XJqayp544bdHH/PHs7/y7/mI2IhhBCiYEjXTK60xkmM0okIxlQoeTht+i7Atlth262UfvlRJoIh1n7i41grXXkIWAghhCgc0iKSK0NRZcS47r2vwvXGNyQPZ4wRARg5Tdg6yOPHn+Ks24XTve1SRyuEEEIUFElEcqUNwipGUMfSDlstCotKaREB+LddVE4eoiziwvqbhxn5yU8ucbBCCCFEYZFEJFc6ygk7nPzA1xn82tfTiuxWy4XBqgC117A6ehI1ZUft/x0j3/v+JQ5WCCGEKCwyRiRXOr6y6tg7X0VJysqqEB8nktY1s/o5lBw5BNxO7C/ez9YNGy9trEIIIUSBkRaRHKnEyqrRN72OoquvTitz2CyEo+mJSOnkUwD88pn0dUeEEEKIy5EkIjlSOoahYhT1/pTImTNpZc7piUjtNZQWhQFw/PxpTvzFX1zKUIUQQoiCI4lIjizaAAxKWr/JxIMPppU57VZCqYnIuhso/qvHMVSMcUsRjg0bLm2wQgghRIGRMSI5sugYMYtB+IvvpPyWXWllTpslPRGxWFBAxDnJ+VXVrP7Td17aYIUQQogCIy0iOVLEx4jYqqqwFBenlcUTkfRpvfzor4jZghgjEO3vv4SRCiGEEIVHEpEcWbRGaQNLZw9TTz2VVuawWQhFjPQT7MVo6wBlp2I8c+uL0VrT+Ys+vvLLADFzp14hhBDiMiFdM7kybLgMhfX8BHpyMq3IabMSik1LRDZ4sFh+y5RtHRu//CX6R6b49H2HAKgtd/L6G2TciBBCiMuHtIjkKKZLKHPVc9W3vk/xDTeklTmztYhsqMduDWKJlFB+++38+ImzWC2KdZVF3P+0dNUIIYS4vEiLSI4sOgbGMNHwBDZHSVqZ025hdCqafkL5GtY7pjgbKeX8N77Jg5Ob2LG1iudvdPGdh0+itUYpdQnvQAghhMgfaRHJkdMyysmRP/Dszbcw+ehj6WW2adN3E3bc/l4UihOf+xLnjp3mmrUVvMBdzbnRECeDkxn1hRBCiJVKEpEc2WJ2nhedoPodDdjXrkkry5i+a56zaQsA1fu+w4FoGVesLuOK2nIAAv3jSx+0EEIIUSAkEcmRVVtYo8Ks/pPd2Gpr08oylnhPeGrMD8CzD99PzNDU1ZaxoaoYh9VCoH/sksQthBBCFAJJRHKkVZQnbTaGDz6CkTFrJss6IoCnbgcQw9j3c64aOsYVq8uwWhRbakoInJcWESGEEJcPSURypFWMUzErpz7wUaLnz6eVOW3WzFkzQFVJFaXOMIbVgdNpY1WZAwB3bSmHJRERQghxGZFEJAdaa8LawVCZgfsnP8a+dm1a+UxjRCYiE4yWTTG+oZhtG43kLJmtqyQREUIIcXmRRCQHMUNjQRGzapybN6Ps9rRypz171wzAYY5w3ljDK/Tvksc2uIo5OzKFISusCiGEuExIIpKDmNY4iVE5HuXMxz6WZYyINetg1WJbMRPOYayDNQyd3Zw8vraiiEhMc348tOSxCyGEEIVAEpEcGAZYNTijMSb+8If4gRSORNeM1uktHEopYiUhxkuqGfHcljy+3hXfNO90cGrpgxdCCCEKgCQiOYhpjUJx3mXgvvdeLKWlaeVOW/zHG56+3wygymJYsHNzZD/89O8BWFdZBMDpYUlEhBBCXB4WtMS7UsoF7AYatda75nlOCxBMfOvSWrcvsHzBz3mpxAyN0gpFDLIsy+60WQEIRY3k10ml8VYS68lx6P8m3NZCdakDh83C6WFZXVUIIcTlYd4tIkopD/GEwAVUz/OcFgCtdafWuhPwK6U6FlC+4Oe8lAxDo7Cw+ZzBszu9GeVmi0i2KbwktqUZO1sDEwPwbC9KxTe/OyMtIkIIIS4T805EtNb+RLIQWMD19wKdKdfoBZrmW36Rz3nJxLRGG3bWVG/GtXt3RrnTPnPXjC6yYWBgee2dsPo6+MP/APEBq9I1I4QQ4nKxZGNElFJu4l0twSxl3rnKc3hep1KqwnwA5Rd7rbkYhgZto+zql7OquSmjPNk1E8mcwmvRZYzbR9ATUbj+DnjqhxAaZVW5k/NjMmtGCCHE5WEpB6u6ZzgeJN7VMlf5xdoLDKc8TuRwrVnFtMaqDEaP9TD15JMZ5Q6zaybLFN6Njv+DcyrM4G8fgRvuhPf+Epzl1JZJIiKEEOLykY9ZM4PMPt5jrvK5tAKVKY+NOVxrVtGYxoIm9MwhTnzggxnl5hiRqSwtIoTXobTBZMQOpTVQezUANaUOzo+FlypkIYQQoqDkIxGZK8nIaVCq1jqktR4xH8BoLtebjaE1Fm3hptWwqePLGeVF9guzZqY7OXqKwxsiTJYkbvfs4/DFm9hoH2FoIkw0y7gSIYQQYqVZykRkpgGmrkTZXOUFLxo1AAvWIgvObdsyyksc8URkMpzZInJuop+Boj8wHgzHFzwrWwPnn+LKsYfQGgbHpVVECCHEyrdkiYjWOgAEE4NSp5f1zlW+VHEtplhiJdUHB0IM/fd/Z5QXJxKRiSyJyNTYRl55eANGRBMaj0LpKlh3PesHHgCQ7hkhhBCXhYtJRLJ2nSil3Oa6IClaAW9KnQZSpuvOo3zW58y3aDT+f8s4TDz8cEZ5caJrZjLLGJGB8RAT110LwOhQYrpu3UupPPUrFIYMWBVCCHFZWMiCZmai0Qx4lFJticTB5E2UJSVWSXUppRoSdXdorZvnWz6P58wrcxzHYy/Yxob29oxyu9WC3aqYDEfTjmutGRwP8e3KfwdgfCiRdNS9FOvkeZ6jjkkiIoQQ4rIw7yXeE10p7YlHtvJOsrRmTFuyvXsh5XM9Z74ZiUTEkrm6e1Kx3ZrRNTMyFSUSA/vIScBgzGwR2fQCeNePOfVvA5KICCGEuCzIpnc5iEbj+8XctP8Jzra2Zq1T7LBmdM0MJJKMG05acU4FGTVbRGwO2PwCXGUlMkZECCHEZUESkRyYXTPDtRaKr78+a50Shy1j1sxAYkbMszdvZHQDF1pEAAL30xr9LOdHZJl3IYQQK58kIjmIJdYHOV1no+JVr8paJ1vXzECitaOyvIaJ4jHGBlO6YaJTvDD0axg+ujRBCyGEEAVEEpEcxGLxrpmy0RiRc+ey1il2ZElExkNYFFw5aGHDM4cZ6R+/ULhhOwC1I48vTdBCCCFEAZFEJAexRNfMDfePMvS1r2WtU+KwZizxPjAWprrUSaW9AuvUOcZHIsmBr5SuYti5ns2TmXvXCCGEECuNJCI5iCa6Zkrf+2dU/cmfZK0T75pJn747MBaiptSB9ao6vn3rKNqAsaEL3TNDVc/nqujT8d19hRBCiBVMEpEcmF0z5Vddj33duqx1snfNhKkpc1BdXM2A7TQAI+cnk+Vnr76TL0Vfy9CEzJwRQgixskkikgOzxeL4v32Uyd//PmudmbpmasqceJ038KUvnAdgZODCLBnrtlv4qeGRKbxCCCFWPElEcpBsEXnsJKG+7Pv0FdttWQer1pQ6KKpexfq/2UtpuS2tRWRVqYM7rT2Ejvxu6YIXQgghCoAkIjkwp+9ua7oV15vemLVOscOSkYgMjoepKXUwadf834rvgSvGyPkLLSI15U7ea/sexX0/XLrghRBCiAIgiUgOzK6Z2CxrvJc4bGldMzFDxxORMifFtmJeFHBSUhxjNKVrpsxp4xm9iaKhQ0sXvBBCCFEAJBHJgdk1c6xjP2O//nXWOtMXNAtOhDE01JQ5sCgLL7nbz+qJcUYGLnTNKKU4Yd9K5cgzS3sDQgghRJ5JIpIDc+2PiavXYVu1KmudksReM2bryWBiefeaUgcAZd//JtYdm5kYDhNNSVjOFtdRET4LU8NLeQtCCCFEXkkikgNzjEj/O15L0dVXZ61TUWwHYHQqvpaIOROmpswJQOvT/8L3B/83XmfwQvdMf8V1/KbsZRCZRAghhFipJBHJQTQWTy5Kjx/HCIWy1qlMJCLDkxEgPmMG4l0zAC+97zQVTzwSr9N/IemIuur4h5IPQvnaJYldCCGEKASSiOQgGosnF9s+9k1Ch7IPLM1IRMbC2K2KcqcNgOoxCI8ex2qzpCUiq8ocqNHTcE6WehdCCLFySSKSg1gk3rox+Ge34Ki7ImudzEQkRE2pE6XiM22GP/I2vn3jJOWrnAyfnUieV1PmYM/EV+AHdy3lLQghhBB5JYlIDmLRxJiOq9djLSvNWqdiWiJybjTEmgpnsnxdaXxp+KIaC8FzKYlIqZNDsXXo808tRehCCCFEQZBEJAdGND7wtGz/IYzJ7INKy502lLqQiJwdmWJ1RVGyvOrT/86Hvx2DyjDBcxeuUVPm4FljA2q8HyYGl/AuhBBCiPyRRCQH2oh3rxT/5FF0NJq1jsWiqCiyE5yMJy1nR9JbRFa9/g38/HmKUPkoo4NTRBOLn60qc/Ks3hCvdP7pJbwLIYQQIn8kEcmB1bYagOt++yDW8vIZ61UW21O6ZqZYU36hRaTa+3ICz61hpKgf9IWZMzVlDg7rtYSdNdIiIoQQYsWSRCQHhqHRaNQsS7xDPBEZmYwQiRmcHwuzJqVrJnz0KG8Lb2fDxloAhs8mEpFSJyEcfHfX/XDNq5buJoQQQog8kkQkB+HRJ4AYh3a/btZ6rpJ4i0j/aHyWzeqUrpngt+/l9v94hNc895U4iqzJAasOm4WKIhsD42EwjCW7ByGEECKfJBHJQbF2YCFGyZXuWetVJLpmzozEZ9mktojUvOudrPnGf/D00NO41pQQTJnCu6rMyRXPfBX+5aaluQEhhBAizyQRyUGRtmNTMTZ/6P2z1qstc3JmeIoTQ/Ful/WVxckya2UlP5/4PQ3fa6Cs1pE+hbfMQX+sFAaehfBExnWFEEKI5U4SkRxMRceJoZkYmn1jum2rSjk2OMFTZ0aoLXdSWWJPlk0+8gjP/cov+ObLvkbV2lKGTk+gdXyDvJpSJ08b6wENA7ITrxBCiJVHEpEcjBgjxGIGp+76+Kz13LWlRGKanx3q58rVZWllsdFRCBzj2vKrWL2hkqnxCBMj5sZ4Dh4LJfaa6ZcpvEIIIVYeSURyMGZ1EbIb1PyVb9Z67tp48vHE6RGuWpM+zbfsllvYtm8f/3lkH/7obwAYODkGxHfoPT5hh7K1ICusCiGEWIFs+Q5gOYtpRdRqUFH/glnrrUsZnHrFtBYR08GzBzEMgxscdzBwcpzN19awqszBwHgI/Z7voirWL2rsQgghRCGQFpEcOMPD2KMGk7/81az1LBbFK65byw2bXLzqeevSykJ9fTx98wvYMVTF00NPU72+7EKLSKmTSEwzUl4HRRVLdh9CCCFEvkgikgN7bJLiUIyRH/14zrpffut2vvNnt1Bd6kg7bqupofo972bjludxbvIcpWusKV0z8bpjz/4a/vuPIRZZ/JsQQggh8kgSkRxoQzNUbrC+9dMXfQ2ry8WqPXu4/rqXAjBS1s/Q6QmMmMGqRCIyOjYGT/0Aho4sRthCCCFEwZBEJBfaQKvcVj3VWjPx0EO4Rg22VmwlYH2SWNRguH+SmtL4CqynbZvilftlwKoQQoiVRRKRHGgN1SMGwe7unK5z9O3vYOxnP+emtTfxu9D9AJw/MUZlsR2rRXEyWglFlTJzRgghxIojiUgObKoWpRSWiosfSKqUou6+H1Dxmtewc8tOAuFncFRY6D86isWiqC51cH48DKuulhYRIYQQK45M381BzOpmsCJMxctektN1HFu3AnBzyc2sKl5FrHaUs0dGAFhd7uTcaAhu+QDYi2a5ihBCCLH8LCgRUUq5gN1Ao9Z61zzPaQGCiW9dWuv2xSzPJ2ukH2tMo7VGKXXR1xn4ylewb9hAxStfyX+96r8490CUB//3MEbMYE1FEedGpuA5r1nEyIUQQojCMO+uGaWUh3gS4gKq53lOC4DWulNr3Qn4lVIdi1Web9axAGvPnmXqiSdyus7k448TPnoUgA1lG1i7rZJoxODw4dOsqXBydiQEE4Nw4KswPrAYoQshhBAFYd4tIlprP/FEoGEB198LbEu5Rq9SqgdoXqTyvKq1FBOrUjjr6nK6zsbPfz7te1UbxlAxfnHwIVZXX8u50XMwNQzf/wtwbYYrvDk934pmGEQnggydPc/Q6fP0P3yIE1WbcBZtwvHMbxk5fYahqmKiWuMcjhBSZZyvcuGMjFF7boSpEkWt3YY9rBmZ0JxYtw6XfR0bTz9EvxFltMaKAlz9UYaqVzNZWU7FxBkqB0YI1TrYUlyJZWKKc1Y7lqs9rLUYWA8/yHgJOKrLKbJacWo7pde+gLLychxnH8PhdGIvK8HqcGJ1OrDWbMRid6Ciofg9WR1gkeFcQoiVacnGiCil3MS7UoJZyrxAIJdyrXXvYsc8G601hqEh/h+G1tiUFZxgKVrcsRtrK1dTs7GMVRPrcG4tYsj6M05Yr2ajrSi++d0VXqJDQ9iqqhb1eZeDwWNP0Pf7n3Lo9HGGRsJEgxYswSpOVq6hOGxn7XAx2lIJylw4bisABiex61XYQmUMWkJELTFWjcewxwxi9kkmAOdYEaNWC6M4cYTBOh4hNhplyAhRMeVCa83YlAIUFVPAsIVoeJKpsIuysRpGLXaOG1aMqI2othE9PcogFuB5mTdy/5GUb6YSD9NRNBoLUSzRCGFHhKg1giMSwx6J0V/txKLCrBocJmaPUlGqKTcMJoajnNi0jtWrr2PNkV6GRocZ2+TAatWUDoaI1bqJbXVTPHqUkqNHULWlbHLVYotpgqqIoufeTK3dQezIg4yqKPbKMmw2O9aYwrH5emxFTmxjZ7FZrdiKS7E6HCiLFWUvBpsNZcSAGEoplMUKSoGyxP8vhBAzWMrBqu4ZjgeJd+/kWp6VUsoJOFMOlQN86UM/xukoxRZTWDSEbfF/HB1R0EphUaBRYCgMi0KjsGiduOhMf41egcX6+EyhzNu5f/xHpp48xOav3J08trGuhmNPDLBqRyX26l/z9h//jv+pqaP2/FNMPfEER+58K5v+9V8o2bGD6MAA9tWrc46jUBgxg+DZcZ4++Cjnn3yK8JlBno5VEzPWUhmxorgWuBYroAhTOjHEaFGMUGmUWDSA3Rqi5JqtbF69GRUeYnhTNc91b6eiyIG2TFHicFBkK8JqsS5KvFprIjHNVCQa///YBOeHz0FZJcooYrjvCU6GBxl32IgEB3EcOc25deuYQlNx9DCOkQmmNmxjla7B8fRTPF2qMVa9kDXBQYqOPs6J1XZCDiurBgzKQ1bOOSuwG1AVCzNlsxGOljERtmELx4ich8PnBghFtmLErIyesmE1bEzE7DBshWdhmC3AFjgDfak38uujiS+yJbiPLfzngoHSmpgl/j6yGok1dywGdgyMqCJsBcNaRHFsAiIGE0UaQ0FRGJSGiSILoCmb0ETs4LRorAbEIoqxIhtWSwmloWEihmIy8fdAyVT8/R22K6wxg5IQTDmhDI2OQsSwMFnkxKEVjtAkYSuE7WDRUDwFE04LMavCGYlhj0LECSVaEwsrQlZFxF5CiRFChWJMOSFmAXs0/hgrsqDQFIc0WoHFpnFoTSxsYcJhQVtLKYuMYkRhvDgeb1E4/sfNpCN+btmkJuSAYmWgYopYVDFWbMemHZSEx4goxVQizy6dhCmHImJV2GMGRWGYKoIybWBEFBEUk84iirTGPhUiZIeIDayx+POOFSm0UhRFDKwGGA5NkdbEQham7IqotYTS2GTitQGtwBGJnz+euNeSKU3UCg7rhddmwmlBq1LKIyPEDMVE4rUpnoKoFUJ2C1ZtUDIFk04ox0BHFVFDMV7kwG5YKQpPELYqQonXpmQKJhyKmBUcUY0zAqEiTYkGIwxhiyJkL6ZYR7CFokw64s9lj4IzAqPF8X/zS8IaDSi7xmGAEVbx66pSymNj6MRroxOvDRomnSmvjR2KLBoVAyMa/z20aCdlkXEixO/HfG3CdkXYprAZ8d+JKSeUao2OKaJaMeFw4NDgCIcI2yBsA6sBxSEYd8Y/i4oiBtYYRJ1QbGiMSPz3MGwtpsQIYYnEmEz8HjoiYEu8NqApDWliFrBawW7o+GvjsGCoEiqiY8RiF34Pi0NgKMWUI/7ZVzqlmXKAEUn9I2lp5GPWzCDxMSbBHMtnshf42PSD0dV/QFWU4BgKYZ80OH3FVaBg69OHsJWXUFO9hkhoipETx4lccwuqcivWw79AD51gcEt8x9zqY6NMVpTTv3ErjqkJNj31FBtvvnp+dz2L4u3bsW/enHZs3RWVPPrzE1xjKWLiaBOR597NR8rgq/1PYb99E2v/+q8p9ng4/dcfJfTss2ztuienAbP5pA3NkSfO8tsHApw4NkTReQsWHX83O1QNjpBi/eQgR2+ooXbtGmp//1PKd1zB1a/eRWmRDRWLYSkpmeezFS96/EopHDaFw5b4dChzsmFtyof5pv+z6M+ZjdaacMwgFDUIR+P/nwiFGI+EmJiYYDw8wZSG0NQEsXNnCRWVUFVWB0NBho8/yugmN5WqGtszf6A/coqRmgoIh6kOnOL02i1MFBdRc/4oleeGGd64lrW6GueZ8xy2hhjbsJ3V4TA1zz7IsVUwUeagZDRCVX+EQ1u3oJSB+8SJ+D+oa2op1aU4jp3i8RoXutKDO3gKa/9xTtc4iFkU1YNhjJiDw2trccYmuep4P5OlNhwlq3CENJHzQU7WrqfEfjWrzzzLYCTIYG3806745CSDZVWcryqjenKI0tOjjFYV47LU4hge53x4klPrn0NtrISqY48yWhxmqNqBPWJQfCrMqVVrmSqys3ngNPbRCJOrq6iMVVBydoD+Uivjq29k08QopSee5LzLSqjIStlIBMsIHNuwHpsRxn3yLFG7wl5diyNqR506x5GaaorK66ntf4Lw0BmCa+Lxrjo7xZStlJNrqimOjlFxdICJSgeVRWtwjEcYDwY5u+EKatR61px4mn41yvDqIhRQfnScgeq1jJQXUTvST8m5cSZrK1ilqrEPjHCCMEMbr2dNTFMW+D0jFZqxcjvOyShF/VHOrNtAzKbYfPYklrBBdE01xboUy8l+zrjKsKzyUD18AsvpAOdr7ETtFiqHwhhhO6c2rcVhTLH12BmipVZKKmopDVsInx3g+Lr1lJc+F9eJPzA0OcjY2vj7ruTUBGNlVZyvcVExOUj5ySCh6iLWOtZgHZlgcGKC4NbnUKsrqDzyJGccU0zUOLFGDcpOTTG4dgNTxU7WDpygOBgiutZFOZVYzg1x1GkhtP5GXFNTFB95lJEqC1MlNhiL4Axqzm3ejEVH2HTqJBqwr6qhQhdhnDjH6dU1lFXVU9HfR6j/BANrnPEE7XyIiCqhf/1qHLExKg+fIeZyUFq2mqKJGGODw5za4qbGuZmaI3/gXDRIaE38XitOTDBau5aRylKqRs5SfnaU6OoyXLYarENjnIqGmdx2PRUxRWXfo5wuixKqcGAPG5SeCzO0cTNRh5V1Z47imIyh11fhohxO9dNXWYplrYeK4X7UiacYWWUn6rRgH47AhJXz2zZhj01SfuwksSILJVW1lEethE4PcHbjOlyVN1Bz/AlGRs4RXBePt+zMJFMl1QysrqZ0agjXsX4maooos63L+LdmsSlt/tU/3xPiY0T2aq23z1HPC/RordW040OAj3jXy0WXJwavZnvebC0iJ4aHh6nIYb2PS21qPMJX7/ol2xvquKPnMVpeb+VLT3+ET5Rdxxvf9D8X6h06hDExSfGNNyyrRGT47Hl+8d2f8tiZCZz9LpyRCrSe4FjVYSLWY7z4sQBnXrWVt7z5byidmATAvm7p3xBCCCEuGBkZobKyEqBSaz2yFM+xlC0igRmOuxJluZZnpbUOASHz++Xw4RwdGCB89CglHk/yWFGpnTXbKhh8dgSH1UKJcTWv2PoKfnP/A7zw8D+w9sMfRilF0TXX5DHyhXnyxDm+8bv76X9mDTc/8zSTdjd22yinq/1snDrE6u3X0fia97GmtDb9xEpXXuIVQgix9JYsEdFaB5RSQaWUW2sdmFbWC5Br+Uox9rOfcfqjf8M1jz+Gsl4Yt7D5uhp+33OM9eudnBqeYs8L3kNn930cH/kV69RdyXpTTzzBwFf+jfVtn0HZ7fm4hexiUYYOP8hvf/t9ip89xKh/E6vX3co6FcXmqsK94UFueE096674W1ik8RpCCCGWl4tJRLKOz0jMkmmYtuBYK+AFOhN1GsyvF6l8RSj3ein2eDJmF2y+roYHv3eYq21OTgYnuariOkLXhviUa5DvGLG0wZaR06eJnj+f/+6L8Dg4SvGffIof3Pt6+oa3cOXoi6kKvhe1HtZvC/Pit9SzevPy6SYTQgixdOY9RsRMNIA7AA/QDjykte5OlDcRH7tRN+28Fi50pezQWvsWs3wecVcAw8ttjAjEB3H+u+9XnK6y8lCl5tvv8vBI5wt4q8vgX3f+K7duvDXfIV5w+g/wy3/kscM/5xOuN1DS7+Dq/uupDK3H6Qiz/TXX8pxb1lFUWkAtNkIIIWZ1KcaILHiw6nKzHBKRyNmzDP77f1D99rdltGjc/82nePShM3ytJsKPrx7i7Cc+xtMfWsdtf/JtKp2VaXXDJ05g37Dh0o6LmRhE93yMJw/8lm+zEzV6A66p1cSsMbY9fzV1Jee5quEWbMWyT44QQiw3y32wqpgnPTXF2P33U/n612UkIlfetIbHfnES51AEe/0O1v3xTVwzfACmJSHjDzzAsXe+C/cPvp/zSq/zillrTh4e5vF//3cCQy/GiL6BIts49tIgz3/wX7nxSx+n7PrnLnkcQgghljdJRAqAY8sW6n54X9aydXWVOKud3DAa5UzpKq541cvQP/opn/1tK1fUXM0br3wjAMU33sjGf/niko8RMQzNr392lId/dATrqMGUej6G4zRV1u/x5r//BNWl5YSe3E7RtdcuaRxCCCFWBklECpxSimtuXc/kdwOc7Pg6W97xGuy+o0R+92nCsXCynqWoiPKdO5csjoH7fsQvn5zimWOVlIQ0FROP8/utj/C+97az+hc/ZuS+QarszviUYklChBBCzJPspFUgAq99HUP33JO17KaXbCKMwXn/EJFz50Ap/vrmv+LN17w5rV74yBFOf+ITGFO5L8kbOXuO0x//OMGRCf7lx0/zrf8KcPKZCiosx3iT6yPcNPFt3njTdq5a58J1xx1s+dp/YnE6576wEEIIkUJaRApExatfNePYDkeRjb41dpy8gLHqOkq+9wEIj3P2FX/PM8FneNGGFwGgIxEmfvMAkVOncLpn2qoHeHY/BI/BNa+BstqsVSL9/Zz7US9/M3IdN4cqKauq48Et/4Oj4uc0v/BTrL3+jpzvWQghhJAWkQKx6r3vpWT7zKvmqyvKmShW7P/6U0TtVXDkV/zPof+m5f4WpqLxFhDnlVfi+tZ3+eoxzSe+9zg/fvxMfMdggCO/gtOPxL/uPwT33QWfuwa63wXnDiWfZ/LRxzgdHOWO3zzMX73sNbx0qpyY8xw/uPHz3Fj2C/7hJZ8HSUKEEEIsEpm+WyDCx4+jI5EZWzJ+8s7345+soqr2pWzaHOHlY2/m5J77ePX+99B6ayuvcb+GQ2dGeOu/PUh0dIzKmkqODExw87ZqOm6P4LrnTVD/bnjlZ+IXnBiER+6BB76IHj6B2v2fRKpu4invTr78Mhujq7zcdPw1jK7p47o3r2XXlbsoikXAWX4JfypCCCHy6VJM35UWkQJxrr2ds62fmbG87IoreNZi4QV3XsXRIza+PfBpKo+eon5NPfc+cy9D42He+e8PccvQs3zzex+l523X8c09NzN27gjGf9/J5BoP7PrkhQuWVMML3svjf/xfvO7q53N+/fP5zsko33rZRjbb93DT8dewvep/8fERXmsvochWJEmIEEKIRSctIgUifPw4ymLBvmFD1vLjgxPc2v4zvvqOep5bXMyP/uHHjEerKL42xj2xr7Bh1dt5sM/BD995PcW/vZ+KV74Sa1kZoa+8gqFTAd7Cp/mHd+xk+5b4Cv1T0SmKbEWMhEf49G8/Q9lj1/HfR1zcFRkjNOVi5+Zvc+UNLtj+Tli9fDbWE0IIsXhkQbPLiGPTphnLYsPDrA4O4iqy8vjJEV66cw1v9j2XJx6z88gvz/LqgfcRURFu2lDB2adCbHzhK7GUlkH/Uzj7H6Os8T+p/WUpb7n7d/yF9yqu3HqUTz/4d/zXq/6L4+ecnOt9Hq/tuYdV2/8USjfwhg88nzVbX34J714IIcTlSlpECsTEww8z9vP7Wf0XH8woG/7f/+VUi49//MCXCTuL+dq7bkqWGYbBG77wWSrGz/Ba55sZPDJJLGLgsEbZcsNatlxTwpYbt4DTwj/+5Cn+45FvU7Suixp1I2Wjb+P40SlebSlmw7Bm4zUuvO+4jlKXTMMVQgghLSKXlciJE4zu76X2gx/I2Cum7Lbb2PL1r/GCqRo+++OnmIrEKLJZoOvtPO708IezV1B77b0896q1fPjP7+L4rw7x5D99g8Gql/HMwfOobx5j9dYK1laeYYc6Q8XA26gaq6d6UlEyWkRxhZ2b/2Qb196yHmW5hPvUCCGEuOzJYNUCUfna11L3/e9n3bDOWllJyY4dvPiqWkJRgwf6BkApYtEwzt//Jy+5chNNz383vz39W7TFYNumM7zy+n/hzQ2neMdnbuH2O6+h33qKM4dG+T/H/4jnPXsjV01YuXpNCdedvJfXbj/HdbdukCRECCHEJSctIstA/xe+SOkLX8CV27dz9Zpy/vOBI7zkmtV8V+3kjfqHfNIzwZrr3s47rnsH9ugUfO8DqLrbMK55I3Zi3KM6+Vbtt3if93287/r4aqxKKXQ0ytCqQ5Tffkue71AIIcTlSlpECkTk7Dme3fUyxh98MO24NgxGf/pTwkePopTifbfX8fOn+vmzb/i56w9rGSipY9PBNhzKil1ZOfWdZv6sOMK5+g/xzC0v4nOfv4Pv9n2XT93yKf70hj9FKRVPQgwDZbNR/dY7sa9Zk6e7FkIIcbmTFpECYa2soOLlL8O2alXacWWx4L7328nvX3f9ep45N8r3HznN/33pVVRf8Tn47z+Gs49DeJyxvh4Gr9pOyZX12D9yFxvWnOZrz/XyvNrnJa+hYzGOvf0dVLzutVTt3n3J7lEIIYSYTmbNFDitddZxI2nGB6C0Jv51/9NQe9Xs1wyH6f/CFyl7ye2UeDyLE6gQQogVR1ZWvcyEjx4lfORI2rH+z/8TR9/29tlPNJMQyEhCxn71a469613ERkcBMCYmUA4Hqz/8IUlChBBC5J0kIgXk9N/8Lf3/7wtpx0puvomKV7/6oq/p2LgBS0UlGAZThw7xzG23M3HwYK6hCiGEEItCumYKSChwGEtJMfa1a5fk+kYoxMBXvsKqPXtQDseSPIcQQoiV41J0zUgiUsCM8XEmDhygeHs91rLSfIcjhBDiMiNjRC4zU089zZlP/R06Go1//8QTHG9+L9HTp/IcmRBCCLE0JBEpILFgkIkHf0csGASguL6eK/b34ti2Lb+BCSGEEEtEumaEEEIIkZV0zVymzOTwlM/H6P79eY5GCCGEWDqSiBSY4+/7U8588pPoaBRjKgQrvMVKCCHE5U2WeC8w5S97GbaaapTNxsZ//qd8hyOEEEIsKWkRKTCuN/wRZS9+MaO9vWjDyHc4QgghxJKSRKQARYeGOPF/38/4r36V71CEEEKIJSVdMwXIUlzMlq9/jeL6+nyHIoQQQiwpSUQKkKWoiJIdO/IdhhBCCLHkpGtGCCGEEHkjiYgQQggh8kYSESGEEELkjSQiQgghhMgbSUSEEEIIkTeSiAghhBAibxY8fVcp1QIEE9+6tNbt8zzHVKO19i1muRBCCCGWpwW1iJgJgda6U2vdCfiVUh1znNMFBLXW7YmkpU8p1bZY5UIIIYRYvpRewO6uSqkhYJvWOphyTGut1Qz13UAfUGWeo5RyAUNAFVCdS3lqHLPEXAEMDw8PU1FRMe97FUIIIS53IyMjVFZWAlRqrUeW4jnm3SKSSCpc2T78lVLeGU5zA6Sek/J1/SKUZ4vTqZSqMB9A+Sy3JYQQQog8WsgYEfcMx4OAa4ayAMRbMbIkMG6gN8fybPYCH5t+cGRkSRI5IYQQYsW6FJ+di7HXzCDxLpQMWuuAUqoX8ALdkN56kmv5DFqBz6V8vxl4dNOmTQu8LSGEEEIkVANLkpUsRiKSNQkxaa13KaXalFLVxJOWQKIosBjlWZ4vBITM75VSxxJfbgRGF3pzy0g5cAK5z5VC7nNluVzuEy6fe73c7nNwqZ5gIYlI1g9+4t0yM5UBkDrdNjHYFODAYpXP0+hSDbQpBEolxwvLfa4Acp8ry+Vyn3D53OtleJ9LZt6DVbXWASCYGLQ6vax3pvOUUp5ph7xAd8osmJzKhRBCCLF8LXRl1VbiiQAASqkGoDPle/e0xccAuqaN62gGfItYLoQQQohlakFjRLTW7UqplkQCArBDa92cUsVLPFFIXW21GfAkWlLqgOZE68pilc8lBHyClHEjK5Tc58oi97myXC73CZfPvcp9LpIFLWgmhBBCCLGYZNM7IYQQQuSNJCJCCCGEyBtJRIQQQgiRN5KICCGEECJvFmNl1bxKTBcOJr51aa3bZ6l+0efkW8q06DqAabOVstU3ZzD1EF9wbhfwkNa6eynjzNXFxr3cXlOlVBewj/g9BlPLZpoVthxe08SCg7uBRq31rizlK+L9Os/7hGX+fp3tPlfSe3WO+1xR79W5fjfz8h7VWi/bB9ACtKR87wU6FvucfD+AtmnfdwA9c5zTAAwBGugDmvJ9H/O81wXHvUxf077EPU5/dC3X1xTwAE2J1+PgYrxOhfjazuM+V8T7dR73uSLeq/O4zxXzXp3rdzNf79G8/2By/KEOEc++Uo/pxT4nz/foIp5Vu1KOeRK/4O5ZzmuYfp/L4XExcS+31zQRX0uWY7P+Y7VcXtNEnNn+QV9R79ds97kS36+zvJ4r6r06y32uiPfqfH438/UeXbZjRBILnLl0lqXeZ9qh92LOKRD1QOrS+mZzoOvSh1JYlvFrmtZEm4h1ofsnLRvyfgUu8/frMn49V9J7dcbfzXy+R5fzGJGMPW8Sgsz8hr+Yc/Iq8QJXTTtsvsBzrTC7Wyk1SHyH5DqdsnlggVtI3MvuNYX0vuXEm9mtZ9mzKcVyfU3l/boy36/yXp1ZQb2e8/jdrJ/h1CBL/B5dzonITMwXfqnPyae9xJe6D85Sxw8X3kRKqSalVJfWuvESxJeLxYp7Ob2mPj3HYMaE5fqazkber3HL8bWV9+rMlsvrmfzdnGWX3SV/jy7brplZXMwv9HJ5E6CUagP2aa07Z6untQ7o9BHd9wANidHhBWsR414Wr2mW3aVntFxf0znI+5Xl+drKe3Vmy+H1nO/vJpfgPbqcE5GZmjlds5RdzDkFI7HZYJ+e33SqhtTvU/4am6kprSBcRNzL+jUlPsWvbz4Vl+trmiDv17nrJi2H11beqzMr9Ndzht/NvL1Hl20iksg2g4k+u+llWfvvLuacQmEO/DGzV6WUK9t9mGVAV2p5SiZesG/4i4l7Ob+mCV6mrU2QzXJ9TU3yfl1Z71d5r86s0F/PmX438/keXbaJSEIrFwbbmFleZ8r37pTFW+Z1TiFKNAl6AH/intzE570PJsrT7jORfbdPaxpsArrn6KfOq/nEvVJe0xRusvzjtMxf05maZVfa+zXrfa7A92vGfa7Q9+pc3QnL/r061+8meXqPqsSc32Ur8UMxX/AdqSOTlVJNxAcX1c33nEKTyKQPk2UEstZaJepk3GfivKaU6jWFfJ+mueJeCa9pKqVUH/HVHP3Tji+71zTxj1oDcAfxf+zambaa5Ep4v852nyvp/TrX67lS3qvz+b1N1FvW79X5/G4m6l3y9+iyT0SEEEIIsXwt964ZIYQQQixjkogIIYQQIm8kERFCCCFE3kgiIoQQQoi8kURECCGEEHkjiYgQQggh8kYSESGEEELkjSQiQgghhMgbSUSEEMuWUqoj8XDNUqcpUadhpjpCiPyx5TsAIUThSCx3fRDo5cKSzQ3EN/vaB9SQ2FdCa709DyEmJZKP4FzLSads7tUGdM9WVwhx6UkiIoRI5QJaU7cHT7Qk9E471pPydRvg1lo3XspAhRArgyQiQohU1WTunBnMUq8r5esesmykJYQQ8yGJiBAilWueW5UHlFIurXVQa9271EEJIVYuSUSEEEnTtz6fpV4vgFLKA5hdM3WJY97EMYA9gJt4S8t2rXVzYivxQeLbrrdm2Vbd3FLcDQTmG1Pi3KbEua7E+UFzjIgQojBJIiKEuGhaa79SykdKV43WujdxrAOoNhMJpVSfUqrNHFyqlAK4G0gOelVKdQH7Us7pUUoFpicr2ZizYlKSJDeJgbVCiMIl03eFEEthkHgrSWq3TWBaHT/xVgsgmTg0TGsB6QKaF/C8jeZUXq11ADiwkKCFEJeetIgIIZbK9MQjCPTNUt8LBBNdO6Y6UpKV2Witu5VSzcCQUspPvGWlfa7zhBD5JYmIEKJQuIiPCUltRVnQQFit9a7EuBUv0KyUQpIRIQqbdM0IIQpFWleNabZVU6fVa4L4uJVE8rGd+IBYIUQBk0RECDEXF4uzTsis10i0hBzIshT77vle30xGUkzvHhJCFBjpmhFCZJWYRltDvJXCa06rTR1MmugG2Qu4lVItWuv2GY61APWJcwLEB7PuJZ48tBGfxhtMdK20KaWqE3VYwPTbYOL6ZiLjJj59WAhRwJTWOt8xCCHEgiW6bPbOtddMSv22+dYVQlw60jUjhBBCiLyRREQIIYQQeSOJiBBiOWtQSnXMNrNGKdWUWLFVCFGAZIyIEEIIIfJGWkSEEEIIkTeSiAghhBAibyQREUIIIUTeSCIihBBCiLyRREQIIYQQeSOJiBBCCCHyRhIRIYQQQuSNJCJCCCGEyJv/DyNKC4O35WP8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x400 with 1 Axes>, <AxesSubplot:xlabel='Time [s]'>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.TDS.plt.plot(ss.GENROU.omega)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}