tutorials/system_dynamics/sd_tutorial.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System Dynamics Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System dynamics is a method devoted to the study of systems, and is thus a tool within the Systems Thinking tool kit. It uses simple graphical notations to model systems such as stock and flow diagrams. These diagrams contain specific components and symbols to describe systems. This tutorial gives an introduction to the elemens of stock and flow diagram using the BPTK-Py framework."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tutorial explains system dynamics with a population model. The model shows how the population changes overtime and which factors influence the population value.\n",
    "\n",
    "![Stock and Flow Diagram of Poplulation Growth](./sfd_population_growth.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Now, let's create the model. It is used to store all stocks, converters and flows. The model runs for 10 years (starttime = 0, stoptime = 10) and we want to analyse the results after each year (dt = 1). In the next steps we are going to add the stocks and flows to the model.\n",
    "\n",
    "We want to simulate how the population changes in the next ten years under external influences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Model\n",
    "from BPTK_Py import sd_functions as sd\n",
    "\n",
    "model = Model(starttime=1.0,stoptime=10.0,dt=1.0,name='Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stocks\n",
    "\n",
    "A stock represents a part of a system whose value at any given instant in time depends on the systems past behavior. The value of the stocks at a particular instant in time cannot simply be determined by measuring the value of the other parts of the system at that instant in time – the only way you can calculate it is by measuring how it changes at every instant and adding up all these changes."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Population Stock](./sfd_population.svg)\n",
    "\n",
    "The stock in our model represents the population at each timestep. Initially, we assume 80 mio. people living in our fictional country.  We create a model by entering `model.stock(<name>)`. The name of our stock is \"population\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = model.stock(\"population\")\n",
    "population.initial_value = 80000000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 786,
       "width": 1592
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "population.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The population does not change. There are no other factors influencing the number of people. To simulate changes in the population, we need births and deaths. We add these factors by using flows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flows\n",
    "\n",
    "Flows represent the rate at which the stock is changing at any given timestep. They either flow into a stock (causing it to increase) or flow out of a stock (causing it to decrease)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Births and Deaths Flows](./sfd_flows.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make this model simple and just suppose 1,000,000 babies are born and 2,000,000 people die each year. The flows are defined by using the method `model.flow(<name>)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "births = model.flow(\"births\")\n",
    "deaths = model.flow(\"deaths\")\n",
    "births.equation = 1000000.0\n",
    "deaths.equation = 2000000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 786,
       "width": 1584
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "births.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 786,
       "width": 1584
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deaths.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equations\n",
    "\n",
    "To connect elements we require equations. These are mathematical operations that are evaluated at each timestep. We combine the flows with our stock by setting the ``equation`` field of ``population``."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stocks and Flows](./sfd_stocks_flows.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our simple example, the population is the sum of births minus the deaths plus the initial value or the value from the last timestep.\n",
    "\n",
    "The Logic in System Dynamics is always the same: Values at timestep ``t`` depend on the result at ``t-1`` Let us look at the population:\n",
    "```\n",
    "\n",
    "population.equation = births - deaths\n",
    "\n",
    "population (1)  = 80,000,0000 (start time)\n",
    "population (2) = population(1) + (births(2) - deaths(2)) = 80,000,000 + (1,000,000 - 2,000,000) = 79,000,000\n",
    "population (3) = population(2) + (births(2) - deaths(2)) = 79,000,000 + (1,000,000 - 2,000,000) = 78,000,000\n",
    "\n",
    "and so on...\n",
    "```\n",
    "\n",
    "See how easy it is to define this behavior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.equation = births - deaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's check whether we are able to obtain the expected results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "population(1): 80000000.0\n",
      "population(2): 79000000.0\n",
      "population(3): 78000000.0\n"
     ]
    }
   ],
   "source": [
    "print(\"population(1): \" + str(population(1)))\n",
    "print(\"population(2): \" + str(population(2)))\n",
    "print(\"population(3): \" + str(population(3)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 786,
       "width": 1592
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "population.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In reality, the number of births or deaths is not fixed, we usually work with ratios. They change depending on the population and external factors (diseases, medical supply, food supply etc.). In system dynamics, we model such behavior with converters. \n",
    "\n",
    "### Converters\n",
    "\n",
    "Converters either represent parts at the boundary of the system (i.e. parts whose value is not determined by the behavior of the system itself) or they represent parts of a system whose value can be derived from other parts of the system at any time through some computational procedure.\n",
    "\n",
    "Let us model the following model in Python"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Image](./sfd_population_growth.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the image above, the converters are represented by circles. In Python, we define converters with ``model.converter`` or ``model.constant``. ``constants`` are converters with a constant value (i.e. they never change).\n",
    "\n",
    "We want to model the birth rate and death rate that are influenced by the food supply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "birthRate = model.converter(\"birthRate\")\n",
    "deathRate = model.converter(\"deathRate\")\n",
    "foodAvailablePerPerson = model.converter(\"foodAvailablePerPerson\")\n",
    "foodAvailable = model.constant(\"foodAvailable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connectors \n",
    "\n",
    "Much like in causal loop diagrams the connectors of a system show how the parts of a system influence each other.  Stocks can only be influenced by flows (i.e. there can be no connector that connects into a stock), flows can be influenced by stocks, other flows, and by converters. Converters either are not influenced at all (i.e. they are at the systems boundary) or are influenced by stocks, flows and other converters.\n",
    "\n",
    "Please note that we do not explicitly model connectors but create the connection by defining equations. Equations are expressive enough to represent interactions between model elements.\n",
    "\n",
    "Since `foodAvailable` is a constant, we can initiliaze it with a float value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodAvailable.equation = 80000000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The converters `foodAvailablePerPerson` and `birthRate` are formulas. `foodAvailablePerPerson` depends on `population` and `foodAvailable`. The more people the less food per person. The birth rate decreases if we have less than one unit food per person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodAvailablePerPerson.equation = foodAvailable / population\n",
    "\n",
    "birthRate.equation = 0.01 * foodAvailablePerPerson\n",
    "\n",
    "births.equation = birthRate * population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the death rate in our model using a non-linear relationship (depending on food available per person). We capture this relationship in a lookup table (often also called \"graphical function\" in the System Dynamics context) that we store in the `points` property of the model (using a Python list)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.points[\"deathRate\"] = [\n",
    "    [0.0,1.0],\n",
    "    [0.1,0.670320046036],\n",
    "    [0.2,0.449328964117],\n",
    "    [0.3,0.301194211912],\n",
    "    [0.4,0.201896517995],\n",
    "    [0.5,0.135335283237],\n",
    "    [0.6,0.0907179532894],\n",
    "    [0.7,0.0608100626252],\n",
    "    [0.8,0.0407622039784],\n",
    "    [0.9,0.025],\n",
    "    [1.0,0.01]\n",
    "]\n",
    "\n",
    "deaths.equation = deathRate * population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily plot the lookup table to see whether it has the right shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 786,
       "width": 1546
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_lookup(\"deathRate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to connect the `deathRate` lookup to the `foodAvailablePerPopulation`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "deathRate.equation = sd.lookup(foodAvailablePerPerson, \"deathRate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have defined all components. Let us plot them. We first register the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BPTK_Py\n",
    "bptk = BPTK_Py.bptk()\n",
    "bptk.register_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1630
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenarios=\"base\",\n",
    "    scenario_managers=\"smPopulation\",\n",
    "    equations=[\"population\",\"deaths\",\"births\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these settings the population is stable, because birth rate and death rate are equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1561
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenarios=[\"base\"],\n",
    "    scenario_managers=\"smPopulation\",\n",
    "    equations=[\"birthRate\",\"deathRate\"],\n",
    "    series_names={}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenarios\n",
    "\n",
    "BPTK Py creates a scenario `base` by default when registering the model. However, it is boring just to examine one scenario. We want to make various assumptions to understand the behavior of the model or the system. Changing values can lead to different outcomes. For this purpose, the BPTK Py framework provides a powerful scenario management enabling us to create different scenarios.\n",
    "\n",
    "We set up a scenario manager using a Python dictionary. The scenario manager identifies the baseline constants of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario_manager = {\n",
    "    \"smPopulation\":{\n",
    "        \"model\": model,\n",
    "        \"base_constants\": {\n",
    "        \"population\": 80000000.0,\n",
    "        \"foodAvailable\": 80000000.0\n",
    "        },\n",
    "        \"base_points\":{\n",
    "          \"deathRate\": [\n",
    "            [0.0,1.0],\n",
    "            [0.1,0.670320046036],\n",
    "            [0.2,0.449328964117],\n",
    "            [0.3,0.301194211912],\n",
    "            [0.4,0.201896517995],\n",
    "            [0.5,0.135335283237],\n",
    "            [0.6,0.0907179532894],\n",
    "            [0.7,0.0608100626252],\n",
    "            [0.8,0.0407622039784],\n",
    "            [0.9,0.0273237224473],\n",
    "            [1.0,0.0183156388887]]\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scenario manager has to be registered as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenario_manager(scenario_manager)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After registering the scenario mangager, we can define and register more scenarios. Let us change `foodAvailable` from 80,000,000 units to 700,000 units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenarios(\n",
    "    scenarios ={\n",
    "        \"scenario07\": {\n",
    "            \"constants\": {\n",
    "                \"foodAvailable\": 70000000.0\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    scenario_manager=\"smPopulation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can plot our scenarios `base` and `scenario07` and see how changing `foodAvailable` affects the population. We plot the `population` for both scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1630
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenarios=[\"base\",\"scenario07\"],\n",
    "    scenario_managers=\"smPopulation\",\n",
    "    equations=[\"population\"],\n",
    "    series_names={}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the stock and flow diagram `foodAvailable` influences `foodAvailablePerPerson`. The less food we have, the less food one person has. `foodAvailablePerPerson` then affects the births and deaths. Less people are born and more people die because we don't have enough food for each person. As a result, the population of `scenario07` decreases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x960 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1561
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenarios=[\"base\",\"scenario07\"],\n",
    "    scenario_managers=\"smPopulation\",\n",
    "    equations=[\"deathRate\"],\n",
    "    series_names={}\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Data as a Pandas Dataframe\n",
    "\n",
    "We built BPTK-Py with integration in mind. This means, you can easily export the simulation results as so-called \"DataFrames\". This is a standard Python exchange format for data. This means you can easily process the simulation results in other data analysis / data science packages for further processing (Machine Learning, Data enrichment and many more). Just add the argument ``return_df=True`` to the plot_scenarios call:"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>smPopulation_base_population</th>\n",
       "      <th>smPopulation_scenario07_population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>8.000000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.838476e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.703903e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.591279e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.496674e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.417206e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.350453e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.294381e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.247280e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>80000000.0</td>\n",
       "      <td>7.207715e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      smPopulation_base_population  smPopulation_scenario07_population\n",
       "t                                                                     \n",
       "1.0                     80000000.0                        8.000000e+07\n",
       "2.0                     80000000.0                        7.838476e+07\n",
       "3.0                     80000000.0                        7.703903e+07\n",
       "4.0                     80000000.0                        7.591279e+07\n",
       "5.0                     80000000.0                        7.496674e+07\n",
       "6.0                     80000000.0                        7.417206e+07\n",
       "7.0                     80000000.0                        7.350453e+07\n",
       "8.0                     80000000.0                        7.294381e+07\n",
       "9.0                     80000000.0                        7.247280e+07\n",
       "10.0                    80000000.0                        7.207715e+07"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenarios=[\"base\",\"scenario07\"],\n",
    "    scenario_managers=\"smPopulation\",\n",
    "    equations=[\"population\"], \n",
    "    series_names={},\n",
    "    return_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for working through this tutorial!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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