long_term_pyomo-nonlinear.ipynb 63 KB
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 "cells": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial VI.1\n",
    "\n",
    "Consider a long-term multi-year investment problem where **CSP (Concentrated Solar Power)** has a learning curve such that\n",
    "\n",
    "$$LCOE = c_0 \\left(\\frac{x_t}{x_0}\\right)^{-\\gamma} + c_1$$\n",
    "\n",
    "where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative\n",
    "capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.\n",
    "\n",
    "Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.\n",
    "\n",
    "We build an optimisation to minimise the cost of supplying a flat demand $d=100$ with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.\n",
    "\n",
    "> **Hint:** Problem formulation is to be found further along this notebook.\n",
    "\n",
    "**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance\n",
    "* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.\n",
    "* A CO$_2$ budget and no learning prefers a system built on nuclear.\n",
    "* A CO$_2$ budget and learning results in a system with CSP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Imports"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 28,
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   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import ConcreteModel, Var, Objective, NonNegativeReals, Constraint, Suffix, exp\n",
    "from pyomo.opt import SolverFactory\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "%matplotlib inline"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 29,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coal</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>CSP</th>\n",
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       "      <th>unit</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>current LCOE</th>\n",
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       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.0</td>\n",
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       "      <td>LCOE EUR/MWh_el</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>specific emissions</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>tCO2/MWh_el</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>potential LCOE</th>\n",
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       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>EUR/MWh_el</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>current volume</th>\n",
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       "      <td>1000000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>200.0</td>\n",
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       "      <td>GW</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
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       "                         coal    nuclear    CSP             unit\n",
       "current LCOE             50.0      100.0  150.0  LCOE EUR/MWh_el\n",
       "specific emissions        1.0        0.0    0.0      tCO2/MWh_el\n",
       "potential LCOE           50.0      100.0   35.0       EUR/MWh_el\n",
       "current volume      1000000.0  1000000.0  200.0               GW"
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      ]
     },
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     "execution_count": 29,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techs = [\"coal\",\"nuclear\",\"CSP\"]\n",
    "colors = [\"#707070\",\"#ff9000\",\"#f9d002\"]\n",
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    "parameters = pd.DataFrame(data=[[50.,100.,150.,\"LCOE EUR/MWh_el\"],\n",
    "                                [1.,0.,0., \"tCO2/MWh_el\"],\n",
    "                                [50.,100.,35., \"EUR/MWh_el\"],\n",
    "                                [1e6,1e6,200,\"GW\"]],\n",
    "                          index=[\"current LCOE\",\"specific emissions\",\"potential LCOE\",\"current volume\"],\n",
    "                          columns=techs+[\"unit\"])\n",
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    "parameters"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 30,
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   "metadata": {},
   "outputs": [],
   "source": [
    "#discount rate\n",
    "rate = 0.05\n",
    "\n",
    "#demand in GW\n",
    "demand = 100.\n",
    "\n",
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    "#learning rate of CSP\n",
    "gamma_csp = 0.4\n",
    "\n",
    "# carbon budget in average tCO2/MWh_el\n",
    "co2_budget = 0.2\n",
    "\n",
    "# considered years\n",
    "years = list(range(2020,2050))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model\n",
    "> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood."
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   ]
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  {
   "cell_type": "code",
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   "execution_count": 31,
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   "metadata": {},
   "outputs": [],
   "source": [
    "model = ConcreteModel(\"discounted total costs\")"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$G_{t,a}$$"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 32,
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   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators = Var(techs, years, within=NonNegativeReals)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$LCOE_{t,a}$$"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 33,
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   "metadata": {},
   "outputs": [],
   "source": [
    "model.costs = Var(techs, years, within=NonNegativeReals)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective is to minimise the system costs:\n",
    "\n",
    "$$\\min \\quad \\sum_{t\\in T, a\\in A} G_{t,a}\\cdot LCOE_{t,a} \\cdot \\frac{8760}{10^6\\cdot (1+r)^{t}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in billion EUR\n",
    "model.objective = Objective(expr=sum(model.generators[tech,year]*model.costs[tech,year]*8760/1e6/(1+rate)**(year-years[0])\n",
    "                                     for year in years\n",
    "                                     for tech in techs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint such that demand is met by generator dispatch:\n",
    "\n",
    "$$\\forall a\\in A: \\quad d = \\sum_{t \\in T} G_{t,a}$$"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 35,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def balance_constraint(model, year):\n",
    "    return demand == sum(model.generators[tech,year] for tech in techs)\n",
    "model.balance_constraint = Constraint(years, rule=balance_constraint)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint on carbon dioxide emissions:\n",
    "\n",
    "$$\\sum_{t\\in T, a\\in A} G_{t,a} \\cdot e_{t} \\leq \\hat{e} \\cdot |A| \\cdot d$$"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 36,
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   "metadata": {},
   "outputs": [],
   "source": [
    "def co2_constraint(model):\n",
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    "    return co2_budget*len(years)*demand >= sum(model.generators[tech,year]*parameters.at[\"specific emissions\",tech] for tech in techs for year in years)\n",
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    "model.co2_constraint = Constraint(rule=co2_constraint)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 37,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "def lcoe_constraint(model,tech,year):\n",
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    "    if tech != \"CSP\":\n",
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    "        return model.costs[tech,year] == parameters.at[\"current LCOE\",tech]\n",
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    "    else:\n",
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    "        return model.costs[tech,year] == parameters.at[\"current LCOE\",tech]*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-gamma_csp)\n",
    "model.lcoe_constraint = Constraint(techs, years, rule=lcoe_constraint)"
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   ]
  },
  {
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
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    "> **Hint:** You can print the model formulation with `model.pprint()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve Model"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 38,
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   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory(\"ipopt\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 39,
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   "metadata": {},
   "outputs": [],
   "source": [
    "results = opt.solve(model,suffixes=[\"dual\"],keepfiles=False)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimised cost:"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 40,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "231.63436487305015\n"
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     ]
    }
   ],
   "source": [
    "print(model.objective())"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unoptimized cost (where everything is supplied by coal) is:"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 41,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1314.0\n"
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     ]
    }
   ],
   "source": [
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    "print(8760*demand*parameters.at[\"current LCOE\",\"coal\"]*len(years)/1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the technology mix of the optimal solution over time:"
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  },
  {
   "cell_type": "code",
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   "execution_count": 42,
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   "metadata": {},
   "outputs": [],
   "source": [
    "capacities = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        capacities.at[year,tech] = model.generators[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 43,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'capacity [GW]')"
      ]
     },
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     "execution_count": 43,
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     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "capacities.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"capacity [GW]\")"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the costs of the technology over time:"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 44,
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   "metadata": {},
   "outputs": [],
   "source": [
    "costs = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        costs.at[year,tech] = model.costs[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 45,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 160)"
      ]
     },
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     "execution_count": 45,
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     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
473
      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "costs.plot(color=colors,ax=ax,linewidth=3)\n",
    "ax.set_xlabel(\"year\")\n",
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    "ax.set_ylabel(\"costs [billion EUR]\")\n",
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    "ax.set_ylim([0,160])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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   "version": "3.6.2"
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}