not_using_initial_conditions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86201dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List, Dict, Union\n",
    "\n",
    "import simpy\n",
    "import pandas as pd\n",
    "import pybamm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pybamm.set_logging_level(\"INFO\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4de7da2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PybammBattery:\n",
    "\n",
    "    def __init__(self, env, capacity, soc=0, Vmin=2.8, Vmax=4.5):\n",
    "        self.env = env\n",
    "        self.capacity = capacity\n",
    "        self.soc = soc\n",
    "        self.excess_power = 0\n",
    "        self.Vmin = Vmin\n",
    "        self.Vmax = Vmax\n",
    "        self.c_n_min = 0\n",
    "        self.c_n_max = 0\n",
    "        self.c_p_min = 0\n",
    "        self.c_p_max = 0\n",
    "        self.step_solution = None\n",
    "        self.neg_soc = []\n",
    "        self.pos_soc = []\n",
    "        \n",
    "        # load solver\n",
    "        self.solver = pybamm.CasadiSolver()\n",
    "        \n",
    "        # load model\n",
    "        self.model = pybamm.lithium_ion.SPMe()\n",
    "        \n",
    "        # load parameter values and process model and geometry\n",
    "        self.parameter_values = pybamm.ParameterValues('Marquis2019')\n",
    "        \n",
    "        geometry = self.model.default_geometry\n",
    "        self.parameter_values['Current function [A]'] = \"[input]\"\n",
    "        self.parameter_values.process_model(self.model)\n",
    "        self.parameter_values.process_geometry(geometry)\n",
    "        \n",
    "        # set mesh\n",
    "        mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, self.model.default_var_pts)\n",
    "\n",
    "        # discretise model\n",
    "        disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n",
    "        disc.process_model(self.model)\n",
    "            \n",
    "        \n",
    "    def update(self, current):\n",
    "        if current == 0:\n",
    "            return 0\n",
    "        \n",
    "        if self.step_solution is not None:\n",
    "            print('condition final time: ', not (\n",
    "            self.step_solution.termination == \"final time\"\n",
    "            or \"[experiment]\" in self.step_solution.termination\n",
    "        ))\n",
    "        \n",
    "        input_parameters= {}\n",
    "        input_parameters['Current function [A]'] = current\n",
    "        self.step_solution = self.solver.step(self.step_solution, self.model, dt=60, npts=100, inputs=input_parameters)\n",
    "        \n",
    "        self.calculate_soc()\n",
    "        \n",
    "        \n",
    "    def calculate_soc(self):\n",
    "        print('negative electrode: ', self.step_solution['Negative electrode SOC'].data[-1])\n",
    "        print('positive electrode: ', self.step_solution['Positive electrode SOC'].data[-1])\n",
    "        print('Discharge capacity: ', self.step_solution['Discharge capacity [A.h]'].data[-1])\n",
    "        \n",
    "        self.neg_soc.append(self.step_solution['Negative electrode SOC'].data[-1])\n",
    "        self.pos_soc.append(self.step_solution['Positive electrode SOC'].data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6eb49569",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate(env: simpy.Environment, battery: PybammBattery, current_delta_list: List[float], records: List[Dict]):\n",
    "    \n",
    "    for current_delta in current_delta_list:\n",
    "        yield env.timeout(1)\n",
    "        battery.update(current_delta)\n",
    "        records.append({\n",
    "            \"power_delta\": current_delta,\n",
    "            \"excess_power\": battery.excess_power,\n",
    "            \"soc\": battery.soc,\n",
    "            \"capacity [A.h]\": battery.capacity\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d33db32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-28 17:11:40,597 - [INFO] base_battery_model.build_model(834): Start building Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:40,665 - [INFO] base_battery_model.build_model(854): Finish building Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:40,687 - [INFO] parameter_values.process_model(415): Start setting parameters for Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:40,791 - [INFO] parameter_values.process_model(518): Finish setting parameters for Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:40,793 - [INFO] discretisation.process_model(137): Start discretising Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:41,039 - [INFO] discretisation.process_model(254): Finish discretising Single Particle Model with electrolyte\n",
      "2022-04-28 17:11:41,041 - [INFO] base_solver.set_up(111): Start solver set-up\n",
      "2022-04-28 17:11:41,232 - [INFO] base_solver.set_up(678): Finish solver set-up\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative electrode:  0.7853715386382283\n",
      "positive electrode:  0.6085626274993166\n",
      "Discharge capacity:  0.01666666666666667\n",
      "condition final time:  False\n",
      "negative electrode:  0.7929783385463505\n",
      "positive electrode:  0.604110061199672\n",
      "Discharge capacity:  0.008000000000000004\n",
      "condition final time:  False\n",
      "negative electrode:  0.7919689747123879\n",
      "positive electrode:  0.6047008824971251\n",
      "Discharge capacity:  0.009150000000000007\n",
      "condition final time:  False\n",
      "negative electrode:  0.7773405133506147\n",
      "positive electrode:  0.6132635099964417\n",
      "Discharge capacity:  0.025816666666666686\n",
      "condition final time:  False\n",
      "negative electrode:  0.791968974712388\n",
      "positive electrode:  0.604700882497125\n",
      "Discharge capacity:  0.00915\n",
      "condition final time:  False\n",
      "negative electrode:  0.8014774745975407\n",
      "positive electrode:  0.5991351746225693\n",
      "Discharge capacity:  -0.001683333333333331\n",
      "   power_delta  excess_power  soc  capacity [A.h]\n",
      "0        1.000             0    0               5\n",
      "1       -0.520             0    0               5\n",
      "2        0.069             0    0               5\n",
      "3        1.000             0    0               5\n",
      "4       -1.000             0    0               5\n",
      "5       -0.650             0    0               5\n"
     ]
    }
   ],
   "source": [
    "# For now let's assume the simple case of one step every second where we first (dis)charge and then implicitly read.\n",
    "# Later we can extend this to a more asynchronous charge/discharge/read pattern with different processes if we want\n",
    "current_delta_list = [1, -0.52, 0.069, 1, -1, -0.65]\n",
    "records = []  # log of some infos for later analysis\n",
    "\n",
    "env = simpy.Environment()\n",
    "battery = PybammBattery(env, capacity=5)\n",
    "env.process(simulate(env, battery, current_delta_list, records))\n",
    "env.run()\n",
    "\n",
    "result = pd.DataFrame(records)\n",
    "with open(\"result.csv\", \"w\") as f:\n",
    "    f.write(result.to_csv())\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81ba8363",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "current = battery.step_solution['Current [A]'].entries\n",
    "terminal_voltage = battery.step_solution['Terminal voltage [V]'].entries\n",
    "step_time_in_seconds = battery.step_solution[\"Time [s]\"].entries\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax1.set_xlabel('time (s)')\n",
    "ax1.set_ylabel('Current [A]', color=color)\n",
    "ax1.plot(step_time_in_seconds, current, color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('voltage [V]', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(step_time_in_seconds, terminal_voltage, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb761a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+4AwgZp+7QI8Yr3W49HeBfxqa/kX2K6TJQBnwqXRkd1JBbQIOSzkey35jCACiQf+OlMN7gJtT2n6yQ9tnOruIaZp3AXcBVFVV5ex1XVxdRc2MGcQfW8qoH/0I5ZA10ED9GnaKRCE46g7AR6bdKfeSi50WJaMYmp5c6z8La6ZppaHpIT1irE3W0SPG91LqXwYcb+/7gROASUA58Iyh6Y/pESNuV/+hHjEe6eraesTYApxqaPo04KN2cViPGGmH2nFyim8lMN4XCI/zBcJlwHzgANtsXyA8OuWwDjDs/WXA2b5AeIhtHHG2XeYqvHW1JGIx9qxY4cj1lSxC5Rwzj634BqqZeYE75Z4MvK1HjA16xEhnrf9c4EF7fwKwQo8YrXrEaADeoO9r/WbKljaOKaho0N8KXIqlWAzg4WjQv8YXCN/gC4Tr7Grf9gXCa3yB8OvAt4EL7LY7gZ9iKbmVwA12mauoOvVUiocNc03oIyH7SDTz/GIAOOWmvV5vaPoRwDggOcJ5HZhlaPogQ9OHY00Dps563WRo+huGpt9iaHp5J/2NMTT938D1wJH2dr2h6S8bmp6WzYCja1DRoH8JsKRD2bUp+1fRuR090aD/XuDerArYT1RJCR7/HHY9uJC2WIxirze315d0Gzknv6OZD7yMurF//pOm9esZc+ut+eqUW6KUWpVyfJe9rNEX5gOP6BGjDUCPGI8bmn4S8AKwDXgRaLPrXgV8gLWedBdwJXBDh/5+C/xOjxj3pRYamv5l4E7SsNqWSBJZxltbh9nSQnxZ7mcgFWpAvWzcwP4pPsHtJBoa2Hbbb2yn3LOdFqevtJqmOTll66icelzrT2E++6f3ANAjxk16xJikR4yzsB7r9Xb5Zj1imHrEaAL+hDWV2JEJHZWT3fYBQEvj3yYKKttUfPQjlB15pCOhj2QElXvyeoovuQY1QB6aAeKUuxIYb2j6OEPTO13rBzA0XQOGYI2SkmXFhqYPs/cnAhOBx+3j0favAs4B3urk2p3qF0PTi7AsCntEFFSWUUrhratl36pXaN7Y1YeLUChINPP8YKA45eoR46C1fj1irDE0/QZD0+tSqs4HFuoRI/XzpBT4l6Hpa7Gm8b5k9wfwF0PT3wTeBIYDN3Zy+cWGpt9taHpVssDe/z0dlna6wu1+UAWBZ24t2269jfjieoZfkrvcMpJuI/ckzfrzMVjsQGLbb35jOeV+/3KnRck6esQ4aK1fjxjXdji+vpN2jViWfJ31mY4/6hXAz4H/GZr+P6xX0mFY/qw/Skd2UVA5oGzsGConn0gsVM+wiy/O2XSCUkqm+HJMPicsLOBprgNoXLeO2N//wdDzz6fssMN6biD0CT1itAA/MDT9GuBou/i/esTYm24fMsWXI7x1dTRv2EDjmrU9V84Q1ghKVFQuSeRztFibQn9kti74JUUeT8E55boNQ9NPMjR9lB4x9tkBYo8HHjQ0/TeGpg9Npw9RUDnCM3MmqrSUWGiR06IIWSSvR1D2byFHHylwp1y38QegGcDQ9NOBIPAAVniktEzhRUHliGKvl+pp04iHl2C2tvbcIBOIFZ9j5KF+KnjanXIPP5yhhemU6zaK9YiRDKDweeAuPWL8XY8YqVN+3SIKKod462pp27GDhhdeyMn1FIiGyjH5HM1cFbiZedIpd8TllzsWG3OAUWxoetLO4Uz2R6iANO0fREHlkKrTT6fI681Z6CPLSKJA3zYupT2jbv7pp7yUOV0KxCk333gQeNbQ9EXAPuBfYKWCZ38U9G4RBZVDisrK8Myexe4nn6RtT4PT4ghZIK8ddW0K8ZNmgDjlugo9YtwEfB+4D5ia4mNVBFyWTh9iZp5jvHV17Fr4ELuffILB55yT1WuJH1Tuyeto5nkoczq0bBkYTrluRI8YL3VStj7d9jKCyjGVxx9P6dixxHMQ+kgpUVC5piBGUAX20Gy7feA45RYaoqByTDL0UcOLL9GyZWt2r4WsQeWavI5mXoD5oNqdcr/4RXHKzUNEQTmAp7YWTJP44sVOiyJkGIlm7i7EKdcdGJp+hKHpM+z9SkPTa9JpJwrKAcrHjaNi4kRi9dm15svXKT7TNNn76mqa33sv76abCmOKz2kJMoM45boDQ9O/DjyC5bgLVsqPR9NpK0YSDuGtq2PLjTfSuG49Fccek7Xr5OO7Ztcjj/DBNVYsy6KaGio0jYoJOuW6ToU+gfIjx6FKSx2WsnPyOpp5PmvVDphtbWy9+WZxynUH38LKF/VvAD1i/MfQ9BHpNBQF5RCeObPZEgwSrw9RcewPsnINpVTefQ03v/suW34eZNBJJ+GpnUujYdC01uDDhx7GbGwEQJWVUX7MMVTouqW4NI2KY4+laNAgh6XfP8WX39HM8+yh6YTYP/9J03/+w5hbbxWnXOdp0iNGs6HpANjOu2k9ZI4qKF8gPAu4DSt51T3RoD/Y4fzlwNeAVqyUw1+NBv3/s8+1YeUiAXg3GvSn5jZxPSVDh1I9dSqx+sUccvnlqCKZbTXb2nj/ygCquJhDfxGk9NBDDzjXHI3SuNag0TBoNNay+/HH2fW3v1kViooo8/nalVaFbo24SoYMyem/IZHHsWLzUOROEadc1/Gsoek/AioNTT8L+CaQ1vqGYwrKFwgXA3cAZwEbgZW+QDgUDfpTw32vBiZHg/69vkD4G8DNWDGdAPZFg/5JuZQ503jratnzzDPsffllqk45JeP9Wy+c/Pka3nHPH9m3ejWHLrj5AOUEoIqLKT/qKMqPOgpv7VzAGq20bt5sKay1Bo2RCHtXv0o8HG5vVzJ6tKW0kopL0yg59NCsTWclrSbzcYovSb6Nujuy494/0bptG2N+c1tBTVvmMQHgQqwBxcVYuanuSaehkyOok4G3o0H/BgBfILwQmAe0K6ho0L88pf5LwJdyKmGWqZ4+naKqKmKh+uwoqDwykmhcu5Ztt99OzexZeObOTauNUorSQw+l9NBDqTnzzPby1g8/pCkSSRltGex55hlIJAArcG95qtLSdcrGjUMVp5WFunvyOZp5HsrckZYtW9lx773UzBanXLegR4wEcLe99QonFdQY4L2U443Ax7upfyHwWMpxhS8QXoU1/ReMBv2PdtZIKXURcBFAmcvmoosqKqiZOZPdy5aRuObHFFVWZrT/fHnhJJqa2HTFFZQMGcLo667r91dvyZAhlEyZQtWUKfuvsW8fTevWHTDa+vAvf8FsbgZAVVRQfqy9rqVPsNa2xo+nqKKiV9duj+WSLze/E/Lkm6ZT2p1yLxenXKexU8J3+TjpEWNiT33khZGELxD+EjAZOCOl+Iho0L/JFwgfCTztC4TfjAb9/+3Y1jTNu7Bzj1RVVbnub89bV0vsH/9gz/LleObMyXj/rvsHd8K2X99C89v/5bC7786aOXBRZSWVkyZROWlSe5nZ2krThg00JZWWYRAPL2HXwoesCsXFlB85rt160FJeGsVeb5fXyetgsclJ4Xx4aDpBMuW6juRUyLfs3z/bv18iE0YSvkD4hDT6aIkG/W/2XO0gNmHlp08y1i7rKMMM4GrgjGjQ35Qsjwb9m+zfDb5A+BmsbI0HKSi3M+jkkykZNYrYolDGFZRCud6PqOHFF9l5//0M+cIXqD5tak6vrUpKqDjmGCqOOQbvvHmAta7VsmkTjWvXtlsQ7v33y8RTItCXjhmTYvauUzFhAiUjRhxgNZmH+ikvlWoqW29eIE65LkKPGP8DMDT9LD1ipM63Xmlo+qtYa1Pd0tMI6llgJd3/vY0DfD1dqBNWAuN9gfA4LMU0HzjAYcEXCB+P5dw1Kxr0b00pHwLsjQb9Tb5AeDjwCSwDirxDFRXhneu3oi3v3EnJ0LQyIafXt8sTFrbF47x/1Y8o8/kY8cPsmNr3FqUUZWPHUjZ2LJ6z91uAte7YQaMRodFY2z7i2v3kU+3DjeKhQ6nQNAaNOoITtlaj1JSuLuF68jE81p5/PUfD888z8qqAOOW6D2Vo+if0iPE8gKHpp5JmkIieFNTKaNA/vbsKvkD46e7Od0U06G/1BcKXAsuwzMzvjQb9a3yB8A3AqmjQHwIWANXA33yBMOw3J9eBP/gC4QTWPzTYwfovr/DU1bHjnj8SDy9h6HmZswNx+wfxBz+9kdZt2/AtfDDj62+ZpmTYMKqnfoLqqZ9oL0s0NNC4bp09PWiNuAa9/DI3tbbSuGYyHHG6gxL3Hrc/L92x4w9/oHTMGIace67ToggHcyFwr6HpXqzH7EPgq+k0VG6fAsokVVVVZkODO/MwbTjnU6iyMsY9/FDG+pz32+cYPKiM+796csb6zBTxxx5j0/cuZ/ill3LIpd/quUGe8Mzqd/B+aR4ls/x87Fc/d1qcXvHYm5v5xl9e5bHvnIY+2uO0OGnTvHET/50xg0O++x2GX3KJ0+LkFKXUXtM0q5yWIx1sBYUeMdJKVgh9iMXnC4SP8gXC1/gC4TW9bSt0jbeujsY33qDpnXcy16lSrpysadmyhc3X/4SKiRMZfvFFTouTURKVg3hh9McoXvEUCdtCMF/I1zWoZNBlz9xahyUROsPQdK+h6b8GngKeMjT9V0ll1RNpKShfIHyoLxD+ni8QXgmssdvN77PEwkF4/H4oKiKewQCyVsJCd6ko0zTZ/KOrMZuaOPQXQdfG1OsrpglPH3YCavdu9jz7rNPi9AmXPTLdYpomsVCIysknUjZ2jNPiCJ1zL7Ab+Jy9xYE/pdOwJyu+i4BzsXyWHsaaS1wUDfp/0h9phYMpHTmCqlNOIRaqZ/hll2XEA96NX8Qf/vWvNDz/PKOuu5byceOcFifjmCasPmQ85pChxEP1eM46y2mRekEy9kj+aKjGNWtp3rCBUefLK8nFHKVHjP+XcvwTQ9NfS6dhTyOo39p1vhAN+n8cDfrfwN2GYXmNp66Wlo0b2bd6tdOiZIWmDRvYuuCXVJ12GoPnF+YA3AQSRcWY089mzzPP0BZLe7rdcdz4QdMT8foQqrQUz6yZTosidM0+Q9PbfUgMTf8EsC+dhj0pqNHAg8CvfIHwOl8g/FOgsOZkXETNjLNQFRXEMpQO3priy0hX/cZsaeH9K66kqLyc0TfdWLAx0pKOuuaMWZgtLcSXLnNYot7jlmemJ8zWVmLhJVR/8pPdOk8LjnMJcIeh6VFD06NYA5+0nNW6VVDRoH9HNOj/fTToPwM4E9gFbPEFwoYvEP5Z/2QWOlJcXUXNjBnEH1uakQV2pdyT8n377/9A41tvMeonP6F0RFqpYPKSdkfdY46l7KijiNVn5mMjF+TbJ0PDiy/Stn073nl5lchgQGFoejFwnh4xjgMmAhP1iHG8HjHeSKd92lZ80aB/YzTo/1U06J+MFdS1sU8SC93iraslEYvRsGJFv/tyywtn3+uvs/33v8c7r24ATMUkQx0V4a2tZd+qV2jeeFCAFCEDxBaFKPJ6qTo9v/zNBhJ6xGgDptr7cT1ixHvTvlsF1VWoo2jQvz4a9N/QXR2hb1SdeirFw4YRW5SZL2+np2sSe/fy/hVXUjJyBCN//GNnhckByftdVER7WpD44sxZZmaTfJp2bdvTwO4nn8QzexZFLgsCLRzEakPTQ4amn2do+qeTWzoNe4ok8SdfIPxJuv8Y/yNWHDwhA6iSEjz+Oex6cCFtsVi/5tbdkG5jy4IFNL/7Loffdx/FNTXOCpMD9ucrVJSOGcOgyZOJLQox7OKL80YBOP3MpMPuJ5/AbGzEWyfTe3lABbADSI1KZAL/6KlhTwrKC7xC9wpqW08XEXqHt24eHz7wZ+LLljHkc5/rcz8KZ9eg9qxYwa4HFzL0gguo+rj7ollkg47RzD3z6vjgmmtpfGsNlR/7qIOS9Ux+qE+LeKie0rFjqZScT/nAPck4fElsS74e6VZBRYN+Xz+EEvpIxUcmUHbkkcRCoX4pKCffOK0ffsj7V19N+fijOeR733VOkBzTMZq5Z+ZMtvz0RsuZ1OUKKolbDGu6omXLVhpeeonhl+TPqHSAczvQcSmos7KDyIt8UAMNpRTeujq23XorzRs39ctD3onpGtM0+eC662nbFePwu+6iqLw890I4RPsUn/3eLPZ4qJ42jXg4zMgrfujqyBlJmd0+xRcPhyGRwFMroY3cjKHpU4BTgUMMTU/NIOnBChDeI72OxSfkhmTa8/4ssCuc8aqOh0LsfvxxDvn2ZVToep/6ME2Txpa2vNtaWq208qlf9t66Wtp27qThhRcycn+zRb4MRmKhEBUTJxZkJJICowwrG0UJUJOyxYHPpNOBjKBcStlYe4E9VN/nBXalwExkQbhuaNm0iQ9+eiOVJ57IsK+mFVG/U65dtIY/v/S/DEqWW0qL9n/7VZ92GsVeL7FQPdVnnNFNK3fg5gFU47r1NEUijLz6aqdFyRsMTZ8F3IY1arlHjxjBDudvAabZh4OAEXrEGGyf+wXgt8/9VI8YD9nl44CFwDAsO4Xz9IhxgPOmHjGeBZ41NP2+ZPLC3pKWgvIFwk9Fg/4zeyoTMounrpYPrr2uzwvsKsdjKDOR4P2rfgSJhBUItjitUXynRHc0cKi3gi9NOSKDEuaGoYPKOGzo/vxWqqyMmjmzif3zUdr2NFBc7c7sCCoPzCTii+uhuBiPP7PZpwsV21H2DuAsYCOw0tD0kB4x2vPn6RHjeyn1L8O2yjY03Y+1TjQJKAeeMTT9MduX6RfALXrEWGho+u+x4rT+rgsx7jE0/bN6xNhl9zsEWKhHjB6dInsKFluBpVGH21ls29d+sQLIClmkfYG9vu8L7Llc8N55/wPsffllRt90I2Vjx/arr4RpMnpwJd/85NEZks5ZvLV17HpwIbufeILBnzrHaXG6xW0R8JOYiQSx+sVUT52a0czTBc7JwNt6xNgAYGj6QqxAC10leD0XuM7enwCs0CNGK9BqaPobwCxD0/+GZTKezIB+P3A9XSuo4UnlBKBHjA8NTU8rnExPa1AXYw3fNPs3uS3CiqckZJFir9deYF+C2dra6/a59INqXL+ebb/+NdVnnon302n54HVLIgFF7v+gT5vK4ydRethhxEKLnBala1x+v/e+vJLWDz6Q0EYHUqKUWpWydUywNgZ4L+V4I10MLgxNPwIYBySzpL+OpZAGGZo+HGsa8DCsab1dtuLqtk+bhKHph3e4Tlpvpp7MzG8DbvMFwpdFg/7b0+lQyCzeulp2P/44DS+8QHUvQ7oolZsJvkRzsxUI1uNh9A0/yYjpb8I0C8qEWCmFt7aW7b/7HS1btlA6cqTTInWJO8dPlnFEUVUV1dOn91x54NBqmubkDPU1H3jEDk+EHjEeNzT9JOAFLH/XF4G2PvR7NfCcoenPYn0GnQaklak0rTWoaNB/uy8QPhXwpbaJBv0P9FrUFHyB8AGLd9GgP9jhfDnwAHAilify56NBf9Q+dxXWvGcb8O1o0J9/YaPToPr0060F9kWh3iuoHH0Sb7/9dpoiEcbeeSclw4ZlpE/TtMIFFRLeulq233kn8cVhhl3YdwOSbJF8Wtw4w5dobGT3smXUzJxJUUWF0+LkE5uwRj1JxtplnTEf+FZqgR4xbgJuAjA0/a/Aeqx38WBD00vsUVR3faJHjKWGpp8AnGIXfVePGNvTET7djLp/Bn6JFfTvJHvrl9b2BcLJxbvZWHOd5/oC4Qkdql0IfBgN+o8GbsFamMOuNx/4CDALuNPur+BQZWXUzJ7F7qeeom1PQ6/bZ3s9Ye+qVey4548M/uxnqJk+recGaZIwTYoKaAQFUObzUXHcxIylU8k0bh6x7nn6aRINDXjrxPepl6wExhuaPs7Q9DKs9+ZBD6Ch6RowBGuUlCwrNjR9mL0/ESsa+eN6xDCB5ew3FT8fa9mnUwxNV1jv6RP0iLEYGGRoelqhZdI1M58MTIgG/Zl8250MvB0N+jcA+ALhzhbv5mEtvgE8AvzWFwgru3xhNOhvAt7xBcJv2/29SAHirZvHroUPsfvJJxh8zjlpt8v2FF/bnj28f2WA0rFjGXFlIKN9F6KCAvDW1bHlpzfSuG4dFcce67Q4XeC+IVQsVE/JyJEMOukkp0XJK/SI0Wpo+qXAMqyZqnv1iLHG0PQbgFV6xEgqq/lYlnWp//mlwL8MTQfLd+lLKetOVwILDU2/EViNFZO1K+4EEliGFTdgpX//O9ZAp1vSVVBvAaOAzWnWT4fOFu8+3lWdaNDf6guEY1gLdGOAlzq07XSRzl40vAigLE+jHicX2OOhUK8UVLbZ8vOf07J5M0f8358zbjptkj+Oo73BM2cOW34etJxNf/hDp8U5ALfe7tadO9nz3HMMu+D8frkuDFT0iLEEWNKh7NoOx9d30q4Ra3arsz43YA0K0uHjesQ4wdD01XbbD+3RXI+kq6CGA2t9gfDLQFOyMBr0u96cxjTNu4C7AKqqqtz3aZgGBy6wb6V0ZHoJ/5RSWVtP2P3kk8T+/g+GXXwxg07IfMaVhElBjqBKhgyh+rTTiC8OM+Lyy135wnXbGlR8yWPQ2oqn1vWvG6FzWmx/LBPA0PRDsEZUPZLuMvT1wDnAz4BfpWz9IZ3Fu/Y6vkC4BCu6+o402xYUntq5YJrEFy/uVbtsvGtat29n8zXXUj5B55BvfTMLV7DWzgrJzDwVb10trVu2sPfll50W5QDc+j0QC4Uo1zQqjj3GaVGEvvEb4J/ACEPTbwKew9IlPZKWgooG/c8CUaDU3l8JvNonUfezEhjvC4TH+QLhrhbvQlgLcGAtyD1tr4OFgPm+QLjcFwiPA8YD7vprzzDl48ZRMXEisfr0Y/MpyPjnsGmabP7xNSQaGhhz882oLE2bFuoaFED1tGkUVVcTC7kzkaGbBlBN77xD4xtv4JXAsHmLHjH+AlwB/BxrmegcPWL8LZ226VrxfR3LSOEPdtEY4NFeS5pCNOhvBZKLdwbwcDToX+MLhG/wBcLJsfwfgWG2EcTlQMBuuwZ4GMugYinwrWjQ3xf7/LzCW1dHUyRC47r1adXPxvt91yOPsOeZZxjx/cspPzp7UR4SCXdblfWHoooKamaeze5ly0js2+e0OO24MdRRvH4xKNUePFnIHwxNH5rcgK3Ag8BfgS12WY+kuwb1LawFsX8DRIP+//gC4fQWQrohGvQftHgXDfqvTdlvBD7bRdt2+/yBgmfObLYEg8TrQ1Qc+4Me62c6El/zu++y5edBBp1yCkPOOy+DPR9MooCn+MAKfRT7+z/Y/fTTeP3+nhvkALel2zBNk1h9PVVTTkl73VVwFa9g2zullCWPTeDInjpIV0E1RYP+Zl8gDLSvB7nkMR44lAwdSvXUqcTqF3PI976X1gJ7pl42Zmsr719xJaq4mEN//jNUlr1ozQI1kkgy6OSTKBk1ilgo5BoFlcQtsfj2rX6NlvfeY3iW1jmF7KJHjH7nQ0n3LfOsLxD+EVDpC4TPAv4GuHMCvcBpX2BfubLHukplLuX7jnv+yL7XXmPUtddSOnp0RvrsDivUUdYv4xiqqAhv7Vwannue1h07nBYHcJ+ZeSy0CFVRQc2Ms5wWRegHhqYrQ9O/ZGj6Nfbx4ek66qaroK7EisX0JlYA2SXAj/sirNA/qqdPp6iqitiinqMRZOqF07h2Ldt++1tqZs/CMzc3X/uFbCSRxFtXB21tlhm1i3DD+Mlsbib+2FJqZsxwbXoSIW3uBKawP/r5bqwoQj3S4xSfHUJoTTTo14C7+yqhkBmsBfaZ1gL7tddQVFnZZd1MRDNPNDay6YorKBkyhNHXXZczwwXTdK/Zc6YoHz+ecl0nFgox9LwvOS2Oq4ZQe1asIBGLSWijwqDPjro9jqBs67h1vkD48J7qCrnBW1dHoqGBPcuX91i3vwpq2y230Pz2fxn9s59RPHhw/zrrBQNhBAXW/2Xjm2/StOEdp0Vpxw1LULFQPcXDhlF16qlOiyL0n6w76g4B1vgC4ad8gXAoufVNVqG/tC+w9zjNp/o1XdPw4ovsvP8Bhnzxi1SfNrUfPfUek8LKB9UVHv8cKCoiVu/8n5NbzMzbYjH2LF+Oxz8HVZKuHZfgYvrsqJvu//41fRRMyALJBfYd9/6J1p07u8wu2p8BSFs8zvtX/YiyceMY8YPv972jPjJQRlClI0ZQNWUK8VA9h3z72476frWbmTu8ChVftgyzpQWvhDYqCPSI8RdD018BzsSaSD5HjxhGOm3TXYP6g70GJbgET20tO+6+h3h4SZfrF4q+mwx/8NMbad22Dd/CB7td58oWheyo2xFvXS3vXxlg36uvMujEE50Wx3EriXionrIjj6Tiox9xVhAhY+gRIwJEettO1qDylIpjjqFc03oV+ihd4kuWEK+vZ/g3v0Hlxz6W8f7ToZBj8XWkZsYMVGWl46GP3HC7mzduYu+qVXjragfMB4rQNelO8SXXoF4G2rPm5UM080LGW1fH1ptvpmnDO5QfebBPXF+s+Fq2bGHzT26gYuJEhl98cYYk7T2FGs28M4qqqqiZMYP40qWMvPpHFDmcFsbJAVQyGLJnrljvCekbSVwDzMVKNpWpaOZCP/H4/VBURHxx51/evV30NhMJNl/1I8zmZg79RdDRBeqEaRZcyvfu8NbVkojF2PPss47J4PSIxTRNYqEQlZNPpGxsp+ndhDzF0PQjDE2fYe9XGppek067tN5AdgRzwWWUjhxB1SmnEAvVM/yyyw56wVgZddP/Hv7wrw/S8MILjLr+OsrH9TtKSb9ImM6/MHNJ1ZQpFA8fTjwUwnOWs5ETnDIzb1yzluYNGxh1wU+cEUDICoamfx0raexQ4Cis9Ei/xzKa6Ja0FJQvEN7N/pF/GVYq4IZo0O/pi8BC5vDU1bI5cBX7Vq/uNHFgui+bpg0b2PrLX1J1+mkM/vznMyxl7xlIa1AAqqQEr38OH/71Qdp27cqpz1m7DA7f71hoEaq0FM/Mmc4KImSaA4KN6xHjP4ampxX9N918UDXRoN9jK6RK4P9hha8QHMZz1lnWAnsnPlHWCKpnzJYW3r/iSorKyxl9442uGLkMFDPzVDx1dZgtLcSXLnPk+sm77YSZudnaSjy8hOpp0yj2enN+fSGrNOkRozl5YGh62sHGez3LHw36zWjQ/yggnzkuoKiqipozzyS+dCmJ5uYDzqW7BrX9d7+n8a23GHXDDZSOcEdag4FkJJGkYsIEyo46KiuWmW6n4YUXaNuxQ0IbFSbPGpr+I6DS0PReBRtPd4rv0ymHRcBkoLG3UgrZwTuvjvjixTSsWEHNjBn7T6ie/aD2vfYa2//wB7zz5uGZeXaWJU2fhBvi7eQYpRTeujorvNTGjZSNHZvj61u/Ttz6WKieIq+XqtNPz/3FhWwTAC7kwGDj96TTMF0zrdTPmlas9O/z0pdPyCbJBfbYotCBCorux9GJvXvZdOWVlIwcwcgfX51dIXvLABxBAXjn+tl2yy2WH9o3vuGIDLnWT217Gtj95JN4z5nnuIm9kBXOAR7QI0avg42na8X3ld52LOSOAxbYY7H2OXwF3b5ttixYQMu773H4ffdRXJOW1WfOKPSMul1ROmYMg046iVionmGXXJLj9UBnbvjuJ5/AbGy00o8IhUgtcIuh6SuAh4ClesRoTadhulN89wPfiQb9u+zjIcCvokH/V/sirS8QHmoL6sMajX0uGvR/2KHOJOB3gAdoA26KBv0P2efuA84AYnb1C6JB/2t9kaVQ8NTWsfP+B4gvXcaQz38O6N5Me8+KFex6cCFDv/IVqj6eVu6wnJIwoWggaigsy8wPrrmWxrfeciSSR64z6sZD9ZSOHUvl8cfn9LpCbtAjxlcMTS8FZgPnAncYmv6EHjG+1lPbdI0kJiaVE4CtTPrzNAWAp6JB/3jgKfu4I3uBL0eD/o8As4BbfYHw4JTzP4wG/ZPs7bV+yFIQVHxkAmVHHnlAVGxF5wOo1g8/5P2rr6Z8/HgO+e53ciZjbyj0jLrd4Zk5E1VWlvPQR07c75YtW2l46SUJbVTg6BGjBXgMWAi8gjXt1yPpKqgie9QEtI+A+hNmYB5wv71/P50IGw3610eD/v/Y++8DW4FD+nHNgia5wL5v1Ss0b9zUXt7xa9g0TT649jradsU4dMHNFJWX51rUtDAH6BoUQLHHQ/W0acTDYcyWlpxfP5fjp3g4DIkEnlqx3itUDE2fbWj6fcB/sFyU7gFGpdM2XSXzK+BFXyD8N/v4s8BNvZQzlZHRoH+zvf8BMLK7yr5A+GQsB+H/phTf5AuEr8UegUWD/qbO2iqlLsLyYqaswBdgPXPnsu3WW4kvrmf4JZd0+kUcW7SI3U88wSHfv5wKzb0B6gfqGlQSb10tu5cto+GFF6g+44ycXNOJ2x0LhaiYONHxyCVCVvky1pLOxXrE6PQ93RXpGkk84AuEVwHT7aJPR4P+td218QXCT9K5ljzAXCwa9Ju+QLjLjzZfIDwa+DNwfjToT2ZhvApLsZUBdwFXYsUJPAjTNO+y61BVVVXQtstlY8cwaPJkYotCDLv44oOm+Fo2bWLLjTdReeKJDPtqn5YPc8ZAdNRNpfq00yj2eoktCuVOQe1PCJUTGtetpykSYeSPf5ybCwqOoEeMc/vaNu1pOlshdauUOtSf0dU5XyC8xRcIj44G/ZttBbS1i3oeIAxcHQ36X0rpOzn6avIFwn8CfpCuXIWOp66WD669jsa31qCUavdpMRMJ3g9cBYmEFQi2uNhZQXtgoMXi64gqK6Nmzmxi//gnbXv2UFxdnbNr5yqSRLw+BCUleObMzsn1hNxiaPpzesSYamh6aqg8sJfH9YjRY6g8p+JFh4Dz7f3zgUUdK/gC4TKsNMEPRIP+RzqcG23/Kqz1q7eyKWw+4Zk1C1VaSixkGUskXzY777ufvStXMvLqq3PuANpbkutmA3mKD6x0KmZTE7sffyIn18vl7TYTCWL1i6meOrXLjNBCfqNHjKn2b40eMTwpW006ygmcU1BB4CxfIPwfYIZ9jC8QnuwLhJMexp8DTgcu8AXCr9nbJPvcX3yB8JtYnsnDgRtzKr2LSV1gL2qzXA0a161n2y23UD3jTLyf/pTDEvZMwv7WGshTfACVkyZRevjhB1hm5oJcWJnvffllWrdskdBGAwBD0/+cTllnOJLwJxr076CTUOvRoH8V8DV7//+A/+ui/fTOygUL77w6dj/+OGM2vEVx0Wjev+IKijweRt9wQ15MmyVkBAXYlpm1tWy/805aPviA0lFpGT7143pZ7f4AYqF6iqqqqJ4uf8oDgI+kHtjBYk9Mp6FzGemErJFcYB//+r+oM6toWreOsXfemTdTKckv+HxQptnGWzuX7XfcQTwcZtiFF+bkmtkeQSX27WP3smXUzJxJUUVFdi8mOIah6VcBySCxcbtYAc3Yhms9IQqqAEkusI97+BGObGtj8Gc/S830aU6LlTb7R1CioMp8PiqPO84KfZRlBdXbDMx9Zc/y5SQaGmR6r8DRI8bPgZ8bmv5zPWJc1Zc+REEVKN7aOnY9uJCtNcPRA1c6LU6v2D+CclYOt+Cpq2XLT2+kcd06Ko49NmvXyZWVeSxUT8moUQw62X0htoTMo0eMqwxNHwKMBypSylf01FYUVIFSefwkVn/y//FozXjOqKpyWpxeIWtQB+KZM4ctPw9aTq0//KHT4vSL1p072fPccwy74HxUkVM2WgMLQ9NnAbcBxcA9esQIdjh/C5CcYhkEjNAjxmD73M2AH8ug7gngO3rEMA1NfwYYDeyz252tR4xO3YUMTf8a8B2sVO+vAacAL7Lfr7ZL5AkpUJRSrD7zs7w31N0m5Z0hU3wHUjJkCNWnnUa8fjFmW1vWr5fNYLHxJY9BayseiVyeEwxNLwbuwArUOgE419D0Cal19IjxPT1iTNIjxiTgduAfdttTgU8AE4GPAidhBelO8sVku66Uk8137Lb/0yPGNKw4rrvSkV8UVAGjlDPJ5/pLQowkDsJbV0vr1q3sffnlrF8rm49MLBSiXNOoOOaYLF5FSOFk4G09Ymyw064vpPtcfucCD9r7JtaUXBlQDpQCW/ogQ6MeMRoBDE0v1yNGBEhrrloUVAGTq0XvTCOOugdTPW0aRdXVxBZlzycq298DTe+8Q+Mbb0jep8xSopRalbJd1OH8GOC9lOONdtlBGJp+BDAOeBpAjxgvAsuBzfa2TI8YRkqTPxma/pqh6dcYmt7d07PR0PTBwKPAE4amLwL+l84/ThRUAaNU7sLWZBJx1D2YoooKamaeze7HHyexb1/PDfpBtkbd8fp6KCrC4/dn5wIDk1bTNCenbGmZb3fBfOARPWK0ARiafjSgY60djQGmG5p+ml33i3rE+Bhwmr2d11WnesT4lB4xdukR43rgGuCPZDjdhpCn5OcUn4ygOsNbN4/E3r3sfurprPSfzRG3aZrEQvVUnXIKpSNHZO06wkFsAg5LOR5rl3XGfPZP7wF8CnhJjxh79IixByuf0xQAPWJssn93A3/FmkrsFEPThyY3rOg/z5HmTLIoqALGGkHlH0kFJWtQBzLopMmUjB6dtdBH+2935p+afatX07JxIx7xfco1K4HxhqaPMzS9DEsJHfQAGZquAUOwrOuSvAucYWh6iZ0R9wzAsI+H2+1Kgbl0Hw/1VWAbsB4rJ9Q2IGpo+quGpncbUUIUVEGTny94U6b4OkUVFeGdO5eG556ndft2p8XpFbFQCFVRQc2Ms5wWZUChR4xW4FJgGWAAD+sRY42h6TcYmp66GDgfWKhHjNSvk0ewcvC9CbwOvK5HjHosg4llhqa/gWU2vgm4uxsxngDm6BFjuB4xhmFZFC4Gvgnc2Z384gdVwOSvFZ9M8XWFt66WHXffTXzJYwz9cpfT/n2i3VE3w89MormZ+GNLqZkxg+Lq/PLJKwT0iLEEWNKh7NoOx9d30q4NuLiT8gbSjKVnc4oeMb6e0v5xQ9N/qUeMiw1N7zaltyiogif/NJSMoLqmfPx4ynWdWH19xhVUkkw/MQ0rVpCIxSS00cBls6HpV2KZuAN8Hthi+2glum4mU3wFjSK/R1CinzrHW1dH45tv0rThnYz2my0jiVionuJhw6g69dSs9C+4ni9gGWc8ipXj7zC7rBgrrVKXyAiqgMnXF7yMoLrH45/D1gULiNWHGPGd72S8/0x+1LTFYuxZvpzB585HlcjrZiCiR4ztwGWGplfZ04OpvN1dWxlBFTAKlYcTfClrUPJ0dkrpiBFUTZlCPFSPmeh2hqRXZON7IL5sGWZLC9667oIXCIWMoemnGpq+FstIA0PTjzM0vVvjiCTyCihwshlXLVu0hzrKUyvEXOCtq6Vl0yb2rV6dsT6TdzuTzt2xUIiyI4+k4iMTeq4sFCq3ADOBHQB6xHgdK1t6jzgy5vYFwkOBhwAfEAU+Fw36P+ykXhuWiSPAu9Ggv84uH4e14DYMeAU4Lxr0N2df8vwi//2gHBbExdTMmIGqrCS2KMSgE3tjUJU7mjduYt+qVzjku98Vn7YBjh4x3jM0PbUorajHTo2gAsBT0aB/PPCUfdwZ+6JB/yR7S7XZ/wVwSzToPxr4EMhNqtE8I19fCaZEM++RoqoqambMIL50KYnmzHybZdrMPL64HgDP3LmZ6VDIV96zI6ObhqaXGpr+A+zpvp5wSkHNA+639+8nzbhMAL5AWGHlEXmkL+0HEkqpPLXis35FQXWPt66ORDzOnmeecVqUg0iGNho0eTJlYzuNTSoMHC4BvoUVz28TMMk+7hGnzGpGRoP+zfb+B8DILupV+ALhVUArEIwG/Y9iTevtigb9rXadLqPzCvm6BiWOuulQNeUUiocPJ15fj+fsszPQo3XDM/HENL61huYNGxh6wfkZ6E3IZ2wrvi/2pW3WFJQvEH4SGNXJqatTD6JBv+kLhLv6mzgiGvRv8gXCRwJP+wLhN4FYb+Sww89fBFBWVtabpgWBCbQl8ktJtbZJLL50UCUleP1+dv71r7Tt2kXx4MEZ6TcTHzWx+hCqtBTPzJkZkEjIRwxNv7ab06YeMX7aUx9ZU1DRoH9GV+d8gfAWXyA8Ohr0b/YFwqOBTrMxRoP+TfbvBl8g/AxWJsa/A4N9gXCJPYrqLjovdvj5uwCqqqry603dT0qLFbsbWznqR0t6ruxCSotFQfWEp66WnfffT3zpMobM/3y/+srU94DZ2ko8vITqadMo9noz06mQj3T0eQKowrIZGAY4p6B6IAScDwTt30UdK/gC4SHA3mjQ3+QLhIdjpR6+2R5xLQc+g2XJ12l7Ac47xYe3spQ8G0ABUFlazJSjhjkthuupmDCBsqOOIhYK9V9BZUimhhdeoG3HDgltNMDRI8avkvuGptdgpX7/CtZ7+1ddtUvFKQUVBB72BcIXYmVW/ByALxCeDFwSDfq/hpUo6w++QDiBZcwRjAb9a+32VwILfYHwjcBqrARYQgcOHzaIS6ePd1oMIYsopfDW1bHtlltofu89yg47rOdGWSYWqqfY66X69LRcXYQCxs4BdTnWGtT9wAl6xDjIpagrVD4uoveVqqoqs6Ghs1GnIOQvLZs28faZMzjkO99m+De+0ed+3tnewLRfPsOtn5/EOcf3ze6obU8D/5k6Fe858xh9/fV9lkVID6XUXtM0XRki3tD0BcCnsZZY7rCTHvYKiSQhCHlO6ZgxDDrpJGKLQo5bbe5+8gnMxkYJbSQAfB84FPgx8L6h6XF7221oejydDiR6oyAUAJ66Wj645loa33qLyo99rE99ZCLUUTwUovSww6g8flKf+xAKAz1i9HsAJCMoQSgAPDNnosrKiC3qfzr4vg7CWrZspeHFl/DW1oqLgJARREEJQgFQ7PFQPW0a8SVLMFta+tRHf3VKfPFiME2x3hMyhigoQSgQvPPqaNu5kz3PP9+vfvo6gorV11Nx3ETKfL5+XV8QkoiCEoQCoXrqVIoHDyYequ9T+/6kN2lct56mSARvbV3PlQUhTURBCUKBoMrK8MyZze6nnqJtT68tevdHM+/DteP1ISgpwTNndh9aC0LniIIShALCU1uL2dTE7sefyNk1zUSCWP1iqqdOpWTo0JxdVyh8REEJQgFROWkSpYcfTizUd2u+3vpS7X35ZVq3bBHjCCHjiB+UIBQQSim8tbVsv/NOWj74gNJRnSUUyCyxUD1FVVVUT5+e9Wt1R7yxhX3NaSVqdR3lJUUMHjTwsi30hCgoQSgwvLVz2X7HHcQXL2bY176Wdru+rEEl9u1j97Jl1MycSVFFRe8EzSCbdu3jjJuX05qPkZGB2R8dxe++dKLTYrgOUVCCUGCU+XxUHnccsVB9rxRUO714x+9ZvpxEQwPeOmet97btbqI1YfLlKUegjfI4KktfOGxopdMiuBJRUIJQgHjqatny0xtpXLeOimOPTatNX6I/xBaFKBk1ikEnn9TrtpkkmZRzujaCTx47wlFZhMwhRhKCUIB45syBkpJehT7qbSy+1p072fPcc3hr56KKnH2VJGzDjuIiCbFUSIiCEoQCpGTIEKpPO4344sWYbdkxHIiHl0BbG55a5633kiOoYokBWFCIghKEAsU7r47WrVvZ++9/p1W/3UgizTWoWH095bpOxTHH9FHCzJFUUEUygiooREEJQoFS/clPUlRdTayPoY+6o2nDOzS+8QZeF4yeYL+CKhEFVVCIghKEAqWoooKaWTPZ/fjjJPbt67F+MhZfOgOo+OJ6KCrC4/f3U8rM0GbKCKoQccSKzxcIDwUeAnxAFPhcNOj/sEOdacAtKUUaMD8a9D/qC4TvA84AYva5C6JB/2vZlVoQ8g9vbR2xR/7O7qeexjs3PWXS0xSfaZrEQvVUnXIKpSPdYTGXkDWogsQpM/MA8FQ06A/6AuGAfXxlaoVo0L8cmATtCu1t4PGUKj+MBv2P5EZcQchPBp00mZLRo4mFFvWooNJ9t+9bvZqWjRsZfum3MiBhZkg66IoVX2Hh1BTfPOB+e/9+4Jwe6n8GeCwa9O/NplCCUGiooiK8c+fS8PwLtG7f3n1d+7cnM/NYKISqrMRz1lkZkrL/JERBFSROjaBGRoP+zfb+B8DIHurPB37doewmXyB8LfAUEIgG/U2dNVRKXQRcBFBWJrGuhIGHt66WHXffTXzJEoZ++cv96ivR3Ez8saXUnHkmRVVVGZKw/7SJH1SXGJo+C7gNKAbu0SNGsMP5W4Bp9uEgYIQeMQbb524G/FiDmSeA7+gRwzQ0/UTgPqASWJIsz7TsWVNQvkD4SaCzSJVXpx5Eg37TFwh3+Q/zBcKjgY8By1KKr8JSbGXAXVjTgzd01t40zbvsOlRVVeVnoC5B6Afl48dTPkEnFqrvXkGlYWbesGIFiVgM7zx3JSZsNzOXNagDMDS9GLgDOAvYCKw0ND2kR4y1yTp6xPheSv3LgOPt/VOBTwAT7dPPYa39PwP8Dvg68G8sBTULeCzT8mdNQUWD/hldnfMFwlt8gfDoaNC/2VZAW7vp6nPAP6NBf0tK38nRV5MvEP4T8IOMCC0IBYq3to6tv/gFTRs2UH7kkX3uJ7YoRPHw4VRNmZJB6fpPm0zxdcXJwNt6xNgAYGj6QqwllrVd1D8XuM7eN4EKrIGAAkqBLYamjwY8esR4ye7zAaxlmowrKKfWoELA+fb++cCibuqeCzyYWmArNXyBsMK6MW9lXkRBKBw8/jlQVNRtnqiezMzbYjH2PPMMXv8cVIm7wniKH1SXjAHeSzneaJcdhKHpRwDjgKcB9IjxIrAc2Gxvy/SIYdjtN6bTZ39xSkEFgbN8gfB/gBn2Mb5AeLIvEL4nWckXCPuAw4BnO7T/iy8QfhN4ExgO3JgLoQUhXykdMYKqKVOI1y/GTCT61Ed86TLMlhY8te6a3oP9sfgGoB9UiVJqVcp2UT/6mg88okeMNgBD048GdGAslgKabmj6af0XOX0c+QyKBv07gDM7KV8FfC3lOEonmjka9DubGU0Q8hDvvDrev+JK9r36KoMmTz7ofPvyTReLULH6EGVHHUXFRyZkUcq+0Wbr3AHoB9VqmubB/5n72YT1kZ9krF3WGfOBVN+BTwEv6RFjD4Ch6Y8BU4A/2/2k02e/kEgSgjBAqDnzTFRlZZehj/abmR9M88ZN7Fv1Ct7a2j6l5cg2bfao0OGg6m5kJTDe0PRxhqaXYSmhg+Z5DU3XgCHAiynF7wJnGJpeYmh6KZaBhKFHjM1A3ND0UwxNV8CX6X6Zps/If6cgDBCKqqqoOWsG8aVLSTR16pXRJfHFllLz1s7Nhmj9Zv8alLzSUtEjRitwKZYVtAE8rEeMNYam32Boeupc7XxgYQdT8UeA/2ItpbwOvK5HjOTXzTeBe7ACKPyXLBhIgCQsFIQBhbe2jnionj3PPovn7LMPOJccGXWc4UuGNho0eTKlY7KyFt5v2myZB+AUX4/oEWMJlil4atm1HY6v76RdG3BxF32uAj6aOSk7Rz43BGEAUTXlFIqHD+/Wmq8jjW+toXnDBjx17ohc3hmJ9nQbDgsiZBT57xSEAYQqKcHr97Pn2RW07dp14Dn71+wwhIrVh1ClpXhmzcqNkH1AIkkUJqKgBGGA4amrhZYW4kuX9ljXbG0lHl5C9bRpFHs8OZCub4ijbmEiCkoQBhgVEyZQdvRRB1nztWfUTSlreOEF2nbscF1oo45IyvfCRBSUIAwwlFJ4a+vY9+qrNL/33kHnU2f4YotCFHu9VJ+WU//MXiMjqMJEFJQgDECS5uKx+v2jKMWBL/e2PQ3sfuopaubMRrk8E0DCNFEKV/poCX1HFJQgDEBKDz2UQSedRDxUv98oosMU3+4nnsBsbMTrwtBGHWlNmDK9V4CIghKEAYp3Xh3N0SiNb77Z6fl4fYjSww6j8vhJuRWsDyQSpkzvFSCioARhgFJz9tmosrJ2Y4l2IwnTpGXLVhpefMm1oY060iYKqiARBSUIA5Rij4fq6dOJL1mC2dJywLn44sVgmnhd7JybSpspU3yFiCgoQRjAeOtqadu5kz3PP3+AiUSsvp6K4yZS5vM5JVqvaEuYAzHVRsEjCkoQBjDVU6dSPHgw8ZTQR4M2RmmKRPLCOCJJW8KUZIUFiCgoQRjAqLIyPHNms/uppzEbGgA45KWnoaQEz5zZDkuXPglTRlCFiCgoQRjgeGprMZua2PfkExSZCQ759zNUT51KydChTouWNm1iZl6QSLoNQRjgVE6aROnhh9MQDvOx0kmU73J/aKOOtIoVX0HiiILyBcKfBa7Hynd/sp3qvbN6s4DbgGLgnmjQH7TLxwELgWHAK8B50aC/OQeiC0LBYYU+qmX7nXfy2UM+pLViENXTpjktVq8QP6jCxKkpvreATwMruqrgC4SLgTuA2cAE4FxfIDzBPv0L4JZo0H808CFwYXbFFYTCxltXC6bJiVvXs/3ET1BUUeG0SL2izZQ4fIWIIyOoaNBvAPgC4e6qnQy8HQ36N9h1FwLzfIGwAUwHvmDXux9rNPa7bMkrCIVO2RFHUD7xOJreeJ1/DPsY/3xwtdMi9YpXojupLCt2Wgwhw7h5DWoMkBpqeSPwcaxpvV3RoL81pbzLPNRKqYuAiwDKXB7wUhCcZPhFX+fp39zHmkOOwtwUc1qcXlFeWsx0bYTTYggZJmsKyhcIPwmM6uTU1dGgf1G2rtsR0zTvAu4CqKqqMnuoLggDFs+MMzlnxpmc47QggmCTNQUVDfpn9LOLTcBhKcdj7bIdwGBfIFxij6KS5YIgCEIB4WY/qJXAeF8gPM4XCJcB84FQNOg3geXAZ+x65wM5G5EJgiAIucERBeULhD/lC4Q3AlOAsC8QXmaXH+oLhJcA2KOjS4FlgAE8HA3619hdXAlc7guE38Zak/pjrv8NgiAIQnZRpjlwlmWqqqrMBjuciyAIQr6jlNprmmaV03JkCzdP8QmCIAgDGFFQgiAIgisRBSUIgiC4ElFQgiAIgisRBSUIgiC4kgFlxaeUSgD7+tC0BGjtsZYziGx9Q2TrGyJb38iWbJWmaRbsQGNAKai+opRaZZrmZKfl6AyRrW+IbH1DZOsbbpbNzRSs5hUEQRDyG1FQgiAIgisRBZUedzktQDeIbH1DZOsbIlvfcLNsrkXWoARBEARXIiMoQRAEwZWIghIEQRBciSioblBKzVJKrVNKva2UCrhAnqhS6k2l1GtKqVV22VCl1BNKqf/Yv0NyKM+9SqmtSqm3Uso6lUdZ/Ma+l28opU5wQLbrlVKb7Pv3mlJqTsq5q2zZ1imlZmZRrsOUUsuVUmuVUmuUUt+xyx2/b93I5ob7VqGUelkp9bot20/s8nFKqX/bMjyklCqzy8vt47ft8z4HZLtPKfVOyn2bZJfn9G8hrzFNU7ZONqAY+C9wJFAGvA5McFimKDC8Q9nNQMDeDwC/yKE8pwMnAG/1JA8wB3gMUMApwL8dkO164Aed1J1g//+WA+Ps//fiLMk1GjjB3q8B1tvXd/y+dSObG+6bAqrt/VLg3/b9eBiYb5f/HviGvf9N4Pf2/nzgoSzet65kuw/4TCf1c/q3kM+bjKC65mTgbdM0N5im2QwsBOY5LFNnzAPut/fvB87J1YVN01wB7ExTnnnAA6bFS8BgpdToHMvWFfOAhaZpNpmm+Q7wNtb/fzbk2mya5qv2/m6sZJxjcMF960a2rsjlfTNN09xjH5bamwlMBx6xyzvet+T9fAQ4UymlcixbV+T0byGfEQXVNWOA91KON9L9H2suMIHHlVKvKKUusstGmqa52d7/ABjpjGjtdCWPW+7npfa0yr0p06GOyGZPOx2P9cXtqvvWQTZwwX1TShUrpV4DtgJPYI3YdpmmmQwhlHr9dtns8zGs7Ns5kc00zeR9u8m+b7copco7ytaJ3EIKoqDyi6mmaZ4AzAa+pZQ6PfWkac0fuMZvwG3yAL8DjgImAZuBXzkliFKqGvg78F3TNOOp55y+b53I5or7Zppmm2mak4CxWCM1zQk5OqOjbEqpjwJXYcl4EjAUuNI5CfMTUVBdswk4LOV4rF3mGKZpbrJ/twL/xPoj3ZKcHrB/tzonIXQjj+P30zTNLfaLJAHczf7pqJzKppQqxVIAfzFN8x92sSvuW2eyueW+JTFNcxewHJiCNT1W0sn122Wzz3uBHTmUbZY9ZWqaptkE/AmH71s+Igqqa1YC420roTKshdaQU8IopaqUUjXJfeBs4C1bpvPtaucDi5yRsJ2u5AkBX7YtmE4BYilTWjmhwzz/p7DuX1K2+bbl1zhgPPBylmRQwB8BwzTNX6eccvy+dSWbS+7bIUqpwfZ+JXAW1hrZcuAzdrWO9y15Pz8DPG2PTHMlWyTlg0NhrY2l3jdH/xbyBqetNNy8YVnbrMea677aYVmOxLKYeh1Yk5QHa179KeA/wJPA0BzK9CDWlE8L1jz6hV3Jg2WxdId9L98EJjsg25/ta7+B9ZIYnVL/alu2dcDsLMo1FWv67g3gNXub44b71o1sbrhvE4HVtgxvAdem/F28jGWg8Teg3C6vsI/fts8f6YBsT9v37S3g/9hv6ZfTv4V83iTUkSAIguBKZIpPEARBcCWioARBEARXIgpKEARBcCWioARBEARXIgpKEARBcCWioAQBUEoNVkp9M+X4UKXUI9216ce1zlFKXdvN+Y8ppe7LxrUFIZ8QM3NBoD323GLTND+ag2u9ANSZprm9mzpPAl81TfPdbMsjCG5FRlCCYBEEjrLz9ixQSvmUnUtKKXWBUupRZeVpiiqlLlVKXa6UWq2UekkpNdSud5RSaqkdzPdfSqmDYsUppY4BmpLKSSn1WaXUW3YuoRUpVeuxopcIwoBFFJQgWASA/5qmOck0zR92cv6jwKexAn/eBOw1TfN44EXgy3adu4DLTNM8EfgBcGcn/XwCeDXl+FpgpmmaxwF1KeWrgNP68e8RhLynpOcqgiAAy00rR9JupVQMa4QDVqiaiXYE8FOBv6WkHSo/uBtGA9tSjp8H7lNKPQz8I6V8K3BoBuUXhLxDFJQgpEdTyn4i5TiB9XdUhJWbaFIP/ezDiqwNgGmalyilPg74gVeUUieaprkDK5bcvgzJLgh5iUzxCYLFbqw0533CtPImvaOU+ixYEayVUsd1UtUAjk4eKKWOMk3z36ZpXos1skqmYTiG/dGvBWFAIgpKEAB71PK8bbCwoI/dfBG4UCmVjDg/r5M6K4DjU9KPL1BKvWkbZLyAFa0eYBoQ7qMcglAQiJm5IOQYpdRtQL1pmk92cb4ceBYrg3JrZ3UEYSAgIyhByD0/AwZ1c/5wICDKSRjoyAhKEARBcCUyghIEQRBciSgoQRAEwZWIghIEQRBciSgoQRAEwZWIghIEQRBcyf8HLfymeSSYJOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neg_soc = battery.step_solution['Negative electrode SOC'].entries\n",
    "pos_soc = battery.step_solution['Positive electrode SOC'].entries\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax1.set_xlabel('time (s)')\n",
    "ax1.set_ylabel('current [A]', color=color)\n",
    "ax1.plot(step_time_in_seconds, current, color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Negative electrode SOC', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(step_time_in_seconds, neg_soc, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a32f1446",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}