intro_to_bptk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Hero](./images/hero.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Business Prototyping Toolkit (BPTK) is a computational modeling framework that enables you to build simulation models using System Dynamics (SD) and/or agent-based modeling (ABM) and manage simulation scenarios with ease.\n",
    "\n",
    "The framework is used to build models of markets, business models, organisations and entire business ecosystems. It can be used to build small, explorative models (as illustrated here) as well as large models with hundreds of thousands of equations.\n",
    "\n",
    "The guiding principle of the framework is to let the modeler concentrate on building simulation models by providing a seamless interface for managing model settings and scenarios and for plotting simulation results. It takes a \"minimalistic\" approach and just provides the necessary modeling and simulation functionality. It standard open source packages for everything else, in particular it relies on environments such as Jupyter to provide plotting and interactive dashboards.\n",
    "\n",
    "* All plotting is done using Matplotlib.\n",
    "* Simulation results are returned as Pandas dataframes.\n",
    "* Numerics via NumPy and SciPy.\n",
    "\n",
    "\n",
    "Model settings and scenarios are kept in JSON files. These settings are automatically loaded by the framework upon initialization, as are the model classes themselves. This makes interactive modeling, coding and testing very painless, especially if using the Jupyter notebook environment.\n",
    "\n",
    "You can find extensive [documentation](https://bptk.transentis.com) online and advanced tutorials and examples on [GitHub](https://github.com/transentis/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Key Components of the Business Prototyping Toolkit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The toolkit has been available since 2018 and meanwhile offers modeling in Python for Agent-based modeling (ABM), System Dynamics Modeling (SD) and hybrid modeling using both SD and ABM.\n",
    "\n",
    "There is also support for importing XMILE models created with external modeling environments, such as [iseesystems](https://www.iseesystems.com) Stella Architect or iThink modeling envirnoments.\n",
    "\n",
    "Once you have a model, you can then define and manage different simulation scenarios, plot the results of those scenarios and build interactive dashboards.\n",
    "\n",
    "The framework also provides a simple REST API server which easily lets you serve up and query models using web technology. You can then use the BPTK Widget Library to build web dashboards.\n",
    "\n",
    "![BPTK Building Blocks](./images/bptk_components.svg)\n",
    "\n",
    "You can view an example dashboard illustrating different COVID-Scenarios [online](http://www.covid-sim.com), the respositories for the [dashboard](https://github.com/transentis/sim-covid-dashboard) and [simulation](https://github.com/transentis/sim-covid-19) are available on [GitHub](https://github.com/transentis/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Model: Simulating Customer Acquisition using the Bass Diffusion Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the different ways of building simulation models using BPTK, we will use a simple model of customer acquistion known as the [Bass Diffusion Model](https://en.wikipedia.org/wiki/Bass_diffusion_model). It describes the process of how new products or services are adopted by consumers.\n",
    "\n",
    "We will build the model using the System Dynamics DSL, using Agent-based modelling, using a hybrid ABM and SD DSL model and using a XMILE.\n",
    "\n",
    "The basic structure of the model is illustrated in the following causal loop diagram:\n",
    "\n",
    "![Customer Acquisition CLD](./images/customer_acquisition_cld.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the Customer Acquisition Model with SD DSL\n",
    "\n",
    "Based on the causal loop diagram, we will implement a stock and flow model with the following stocks, flows, converters and constants:\n",
    "\n",
    "![Customer Acquisition CLD](./images/customer_acquisition_stock_flow.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building The Model\n",
    "\n",
    "Setting up the model using the SD DSL is quite easy. We first instantiate a model class, which will be the container that holds the model elements. This ensures you can run multiple models in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Model\n",
    "from BPTK_Py import sd_functions as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(starttime=1.0,stoptime=60.0, dt=1.0, name=\"Customer Acquisition SDDSL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stocks\n",
    "customers = model.stock(\"customers\")\n",
    "potential_customers = model.stock(\"potential_customers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#flows\n",
    "customer_acquisition=model.flow(\"customer_acquisition\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converters\n",
    "acquisition_through_advertising = model.converter(\"acquisition_through_advertising\")\n",
    "acquisition_through_word_of_mouth = model.converter(\"acquisition_through_word_of_mouth\")\n",
    "consumers_reached_through_advertising = model.converter(\"consumers_reached_through_advertising\")\n",
    "consumers_reached_through_word_of_mouth= model.converter(\"consumers_reached_through_word_of_mouth\")\n",
    "market_saturation = model.converter(\"market_saturation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#constants\n",
    "initial_customers = model.constant(\"initial_customers\") \n",
    "initial_potential_customers = model.constant(\"initial_potential_customers\") \n",
    "advertising_success = model.constant(\"advertising_success\")\n",
    "consumers_reached_per_euro = model.constant(\"consumers_reached_per_ruro\")\n",
    "advertising_budget = model.constant(\"advertising_budget\")\n",
    "word_of_mouth_success = model.constant(\"word_of_mouth_success\")\n",
    "contact_rate = model.constant(\"contact_rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined all the model elements, we need to define the actual equations. The neat thing is that we can write these equations directly using the model elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#equations\n",
    "customers.equation = customer_acquisition\n",
    "potential_customers.equation = -customer_acquisition\n",
    "customer_acquisition.equation=sd.min(potential_customers,acquisition_through_advertising+acquisition_through_word_of_mouth)\n",
    "acquisition_through_advertising.equation = advertising_success*consumers_reached_through_advertising\n",
    "consumers_reached_through_advertising.equation = consumers_reached_per_euro*advertising_budget*(1-market_saturation)\n",
    "market_saturation.equation = customers/(customers+potential_customers)\n",
    "acquisition_through_word_of_mouth.equation = word_of_mouth_success*consumers_reached_through_word_of_mouth\n",
    "consumers_reached_through_word_of_mouth.equation=contact_rate*customers*(1-market_saturation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to initiale the stocks and set the constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize model\n",
    "customers.initial_value=initial_customers\n",
    "potential_customers.initial_value=initial_potential_customers\n",
    "initial_customers.equation = 0.0\n",
    "initial_potential_customers.equation = 60000.0\n",
    "advertising_success.equation = 0.1\n",
    "consumers_reached_per_euro.equation = 100.0\n",
    "advertising_budget.equation = 100.0\n",
    "word_of_mouth_success.equation = 0.01\n",
    "contact_rate.equation = 10.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is now complete and we can directly plot the behavour of the model elements over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course you can also access the underlying Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>2081.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3247.916662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>4500.994779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>5842.312754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>7272.284453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>8790.164623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>10393.900018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>12080.003090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         customers\n",
       "1.0       0.000000\n",
       "2.0    1000.000000\n",
       "3.0    2081.666667\n",
       "4.0    3247.916662\n",
       "5.0    4500.994779\n",
       "6.0    5842.312754\n",
       "7.0    7272.284453\n",
       "8.0    8790.164623\n",
       "9.0   10393.900018\n",
       "10.0  12080.003090"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.plot(return_df=True)[1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For debugging purposes it can be useful to take a look at the internat representation of the model equations - these are stored as Python lambda functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lambda model, t : ( (model.memoize('initial_customers',t)) if (t <= model.starttime) else (model.memoize('customers',t-model.dt))+ model.dt*(model.memoize('customer_acquisition',t-model.dt)) )\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.function_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lambda model, t : max( 0,min( model.memoize('potential_customers',t), model.memoize('acquisition_through_advertising',t)+model.memoize('acquisition_through_word_of_mouth',t)))\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_acquisition.function_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Up Scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scenarios are just particular settings for the constants and graphical functions in your model and scenario managers are a simple way of grouping scenarios.\n",
    "\n",
    "You can create scenarios directly in Python (which we will do here), but the easiest way to maintain them is to keep them in separate JSON files – you can define as many scenario managers and scenarios in a file as you would like and use as many files as you would like.\n",
    "\n",
    "Each scenario manager references the model it pertains to. So you can run multiple simulation models in one notebook.\n",
    "\n",
    "All the scenario definition files are kept in the scenarios/ folder. The BPTK_Py framework will automatically scan this folder and load the scenarios – including the underlying simulation models – into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario_manager={\n",
    "    \"sddsl_customer_acquisition\":{\n",
    "        \"model\":model,\n",
    "        \"base_constants\":{\n",
    "            \"initial_customers\" : 0.0,\n",
    "            \"initial_potential_customers\" : 60000.0,\n",
    "            \"advertising_success\": 0.1,\n",
    "            \"consumers_reached_per_euro\" : 100.0,\n",
    "            \"advertising_budget\" : 100.0,\n",
    "            \"word_of_mouth_success\": 0.01,\n",
    "            \"contact_rate\" : 10.0\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To manage the scenarios you need to instantiate the bptk class - this class stores the scenario managers and scenarios and provides lot's of convenient functions to plot data, export model results or import data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BPTK_Py\n",
    "bptk = BPTK_Py.bptk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenario_manager(scenario_manager)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convenient feature of scenarios is that you only have to define variables that change - essentially the scenario manager first takes the constants as set in the model itself and then overrides them with the settings from the scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenarios(\n",
    "    \n",
    "    scenario_manager=\"sddsl_customer_acquisition\",\n",
    "    scenarios=\n",
    "    {\n",
    "        \"base\":{\n",
    "            \n",
    "        },\n",
    "        \"low_word_of_mouth\":{\n",
    "            \"constants\":{\n",
    "                \"word_of_mouth_success\":0.001\n",
    "            }\n",
    "        },\n",
    "        \"high_word_of_mouth\":{\n",
    "            \"constants\":{\n",
    "                \"word_of_mouth_success\":0.1\n",
    "            }\n",
    "        },\n",
    "        \"interactive_scenario\":{}\n",
    "        \n",
    "    }\n",
    "    \n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenario_managers=[\"sddsl_customer_acquisition\"],\n",
    "    scenarios=[\"base\",\"low_word_of_mouth\",\"high_word_of_mouth\"],\n",
    "    equations=[\"customers\"],\n",
    "    series_names={\n",
    "        \"sddsl_customer_acquisition_base_customers\":\"Base\",\n",
    "        \"sddsl_customer_acquisition_low_word_of_mouth_customers\":\"Low Word of Mouth\",\n",
    "        \"sddsl_customer_acquisition_high_word_of_mouth_customers\":\"High Word of Mouth\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xmile_customer_acquisition': ['base',\n",
       "  'low_word_of_mouth',\n",
       "  'high_word_of_mouth'],\n",
       " 'sddsl_customer_acquisition': ['base',\n",
       "  'low_word_of_mouth',\n",
       "  'high_word_of_mouth',\n",
       "  'interactive_scenario']}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bptk.get_scenario_names([],format=\"dict\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building An Interactive UI\n",
    "\n",
    "It is easy to build interactive dashboards in Jupyter using IPywidgets – all you need from BPTK is the ability to plot graphs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfc0b70ca32f45e68440483696f19070",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Output(), Output(), Output()), _titles={'0': 'Customers', '1': 'Customer Acquisition', '2': 'Sce…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0038aca8e53c46cbaf7fe346b89f220a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.01, continuous_update=False, description='Word Of Mouth Success', ma…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customerTab = widgets.Output()\n",
    "customerAcquisitionTab = widgets.Output()\n",
    "scenariosTab = widgets.Output()\n",
    "\n",
    "\n",
    "tabs = widgets.Tab(children = [customerTab, customerAcquisitionTab,scenariosTab])\n",
    "tabs.set_title(0, 'Customers')\n",
    "tabs.set_title(1, 'Customer Acquisition')\n",
    "tabs.set_title(2, 'Scenarios')\n",
    "display(tabs)\n",
    "\n",
    "@interact(word_of_mouth_success=widgets.FloatSlider(\n",
    "    value=0.01,\n",
    "    min=0.001,\n",
    "    max=0.1,\n",
    "    step=0.001,\n",
    "    continuous_update=False,\n",
    "    description='Word Of Mouth Success'\n",
    "))\n",
    "def dashboardWithTabs(word_of_mouth_success):\n",
    "    scenario= bptk.get_scenario(\"sddsl_customer_acquisition\",\"interactive_scenario\")\n",
    "    \n",
    "    scenario.constants[\"word_of_mouth_success\"]=word_of_mouth_success\n",
    "    bptk.reset_scenario_cache(scenario_manager=\"sddsl_customer_acquisition\",\n",
    "                                                             scenario=\"interactive_scenario\")\n",
    "\n",
    "\n",
    "\n",
    "    with customerTab:\n",
    "        # turn of pyplot's interactive mode to ensure the plot is not created directly\n",
    "        plt.ioff()\n",
    "        # clear the widgets output ... otherwise we will end up with a long list of plots, one for each change of settings\n",
    "        customerTab.clear_output()\n",
    "        # create the plot, but don't show it yet\n",
    "        bptk.plot_scenarios(\n",
    "            scenario_managers=[\"sddsl_customer_acquisition\"],\n",
    "            scenarios=[\"interactive_scenario\"],\n",
    "            equations=['customers'],\n",
    "            title=\"Customers\",\n",
    "            freq=\"M\",\n",
    "            x_label=\"Time\",\n",
    "            y_label=\"No. of Customers\"\n",
    "            )\n",
    "        # show the plot\n",
    "        plt.show()\n",
    "        # turn interactive mode on again\n",
    "        plt.ion()\n",
    "\n",
    "    with customerAcquisitionTab:\n",
    "        plt.ioff()\n",
    "        customerAcquisitionTab.clear_output()\n",
    "        bptk.plot_scenarios(\n",
    "            scenario_managers=[\"sddsl_customer_acquisition\"],\n",
    "            scenarios=[\"interactive_scenario\"],\n",
    "            equations=['customer_acquisition'],\n",
    "            title=\"Customer Acquisition\",\n",
    "            freq=\"M\",\n",
    "            x_label=\"Time\",\n",
    "            y_label=\"No. of Customers\"\n",
    "            )\n",
    "        plt.show()\n",
    "        plt.ion()\n",
    "    \n",
    "    with scenariosTab:\n",
    "        plt.ioff()\n",
    "        scenariosTab.clear_output()\n",
    "        bptk.plot_scenarios(\n",
    "            scenario_managers=[\"sddsl_customer_acquisition\"],\n",
    "            scenarios=[\"base\",\"low_word_of_mouth\",\"high_word_of_mouth\",\"interactive_scenario\"],\n",
    "            equations=[\"customers\"],\n",
    "            series_names={\n",
    "                \"sddsl_customer_acquisition_base_customers\":\"Base\",\n",
    "                \"sddsl_customer_acquisition_interactive_scenario_customers\":\"Interactive\",\n",
    "                \"sddsl_customer_acquisition_low_word_of_mouth_customers\":\"Low Word of Mouth\",\n",
    "                \"sddsl_customer_acquisition_high_word_of_mouth_customers\":\"High Word of Mouth\",\n",
    "        }),\n",
    "        plt.show()\n",
    "        plt.ion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: especially for larger models it is much better to keep the model and the scenario definitions in separate files, which of course is also possible. See the online [documentation](https://bptk.transentis.com) and  [detailed tutorial](https://github.com/transentis/bptk_py_tutorial) for illustrations of how this can be done. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Customer Acquisition using Agent-based Modeling\n",
    "\n",
    "The basic concept behind Agent-based models is quite simple: you populate an environment (the model) with a set of agents. Agents and the environment each have a set of properties and each agent must always be in a defined state.\n",
    "\n",
    "Agents can perform actions and interact amongst each other and with the environment by sending each other events - the agents react to these events by updating their properties and/or changing their state.\n",
    "\n",
    "![Agents and their environment](./images/agents_environment.svg)\n",
    "\n",
    "So to create an agent using Python and the BPTK framework, all you really need to do is:\n",
    "\n",
    "* Identify the relevant agents\n",
    "* Define the agents properties \n",
    "* For each agent, implement an initializer which sets the agents initial state\n",
    "* Define handlers for each kind of event you want your agent to react to\n",
    "* Define an action method, which describes what the agent does in each time-step, e.g. perform internal tasks and send events to other agents\n",
    "\n",
    "Defining the model is even easier:\n",
    "\n",
    "* Define the environment properties and update them when necessary\n",
    "* Tell the model which kinds of agents there are\n",
    "* Then, to configure the simulation, all we need to do is to set the initial values of the properties and instantiate the initial agents. Each unique configuration of a model is refered to as a scenario. The BPTK framework helps you to manage different scenarios and compare results easily.\n",
    "\n",
    "Configuring Agent-based models is best done using a config file defined in JSON.\n",
    "\n",
    "Agent-based modeling is a very powerful approach and you can build models using ABM that you cannot build using SD. This power comes at a prices though: because each agent is modelled as an individual entity, agent-based models are quite slow.\n",
    "\n",
    "## Setting Up the Model\n",
    "\n",
    "For the customer acquisition model, we really just need to agents: the company that sends advertising events and the consumers that receive those events. If a consumer becomes a customer, then the consumers starts sending word ouf mouth events.\n",
    "\n",
    "This is illustrated in the following diagram:\n",
    "\n",
    "![Customer Acquisition ABM](./images/customer_acquisition_abm.svg)\n",
    "\n",
    "Note that the advertising budget and the contact rate are properties of the respective agents – this is a glimpse at the power of agent-based modeling, because we could easily set individual contact rates for the consumers or have multiple companies advertising competing products. All these aspects cannot easily be modelled using System Dynamics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Model\n",
    "from BPTK_Py import sd_functions as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Agent\n",
    "from BPTK_Py import Model\n",
    "from BPTK_Py import Event\n",
    "from BPTK_Py import DataCollector\n",
    "from BPTK_Py import SimultaneousScheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up agents is actually quite simple - all you need to do is register handlers for the events and define the `act` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Consumer(Agent):\n",
    "    def initialize(self):\n",
    "        self.agent_type = \"consumer\"\n",
    "        self.state = \"potential\"\n",
    "        self.register_event_handler([\"potential\"],\"advertising_event\",self.handle_advertising_event)\n",
    "        self.register_event_handler([\"potential\"],\"word_of_mouth_event\",self.handle_word_of_mouth_event)\n",
    "        \n",
    "    def handle_advertising_event(self,event):\n",
    "        if self.is_event_relevant(self.model.advertising_success):\n",
    "            self.state=\"customer\"\n",
    "    \n",
    "    def handle_word_of_mouth_event(self, event):\n",
    "        if self.is_event_relevant(self.model.word_of_mouth_success):\n",
    "            self.state=\"customer\"\n",
    "                \n",
    "    def act(self,time,round_no,step_no):\n",
    "        # consumers who are customers generate word of mouth events\n",
    "        if self.state == \"customer\":\n",
    "            self.model.random_events(\n",
    "                \"consumer\",\n",
    "                self.contact_rate,\n",
    "                lambda agent_id: Event(\"word_of_mouth_event\", self.id, agent_id)\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the company agent defined below accesses the properties `advertising_budget`and `consumers_reacher_per _euro`. These properties are defined in the scenarios and can then be accessed \"magically\" as either agent properties or model properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Company(Agent):\n",
    "    def initialize(self):\n",
    "            self.agent_type=\"company\"\n",
    "            self.state = \"active\"\n",
    "    \n",
    "    def act(self,time,round_no,step_no):\n",
    "        self.model.random_events(\n",
    "            \"consumer\",\n",
    "            self.advertising_budget*self.model.consumers_reached_per_euro,\n",
    "            lambda agent_id: Event(\"advertising_event\",self.id, agent_id)\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model itself needs a way of instantiating agents - for this you register agent factories, which return an agent of a particular type. In their simplest form an agent factory is just a lambda function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomerAcquisitionAbm(Model):\n",
    "    def instantiate_model(self):\n",
    "        self.register_agent_factory(\"consumer\", lambda agent_id,model,properties: Consumer(agent_id, model,properties))\n",
    "        self.register_agent_factory(\"company\", lambda agent_id,model, properties: Company(agent_id, model, properties))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_abm=CustomerAcquisitionAbm(1,60,dt=1,name=\"Customer Acquisition Agent-based Model\",scheduler=SimultaneousScheduler(),data_collector=DataCollector())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_abm.instantiate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_abm_config =  {\n",
    "             \"runspecs\": {\n",
    "                  \"starttime\": 1,\n",
    "                  \"stoptime\":60,\n",
    "                  \"dt\": 1.0\n",
    "            },\n",
    "            \"properties\":\n",
    "            {\n",
    "                \"word_of_mouth_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.01\n",
    "                },\n",
    "                \"advertising_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.1\n",
    "                },\n",
    "                \"consumers_reached_per_euro\":\n",
    "                {\n",
    "                    \"type\":\"Integer\",\n",
    "                    \"value\":100\n",
    "                }\n",
    "                \n",
    "            },\n",
    "            \"agents\":\n",
    "            [\n",
    "                {\n",
    "                    \"name\":\"company\",\n",
    "                    \"count\":1,\n",
    "                    \"properties\":{\n",
    "                         \"advertising_budget\":\n",
    "                        {\n",
    "                            \"type\":\"Integer\",\n",
    "                            \"value\":100\n",
    "                        }\n",
    "                    }\n",
    "                },\n",
    "                {\n",
    "                    \"name\":\"consumer\",\n",
    "                    \"count\":60000,\n",
    "                    \"properties\":{\n",
    "                        \"contact_rate\":\n",
    "                        {\n",
    "                        \"type\":\"Integer\",\n",
    "                        \"value\":10\n",
    "                        }\n",
    "                    }\n",
    "                        \n",
    "                }\n",
    "            ]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_abm.configure(customer_acquisition_abm_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: Running the agent-based model takes a little time, ca. 3 min on my machine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_abm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'company': {'active': {'count': 1,\n",
       "    'advertising_budget': {'total': 100,\n",
       "     'max': 100,\n",
       "     'min': 100,\n",
       "     'mean': 100.0}}},\n",
       "  'consumer': {'potential': {'count': 60000,\n",
       "    'contact_rate': {'total': 600000, 'max': 10, 'min': 10, 'mean': 10.0}}}},\n",
       " {'company': {'active': {'count': 1,\n",
       "    'advertising_budget': {'total': 100,\n",
       "     'max': 100,\n",
       "     'min': 100,\n",
       "     'mean': 100.0}}},\n",
       "  'consumer': {'potential': {'count': 58982,\n",
       "    'contact_rate': {'total': 589820, 'max': 10, 'min': 10, 'mean': 10.0}},\n",
       "   'customer': {'count': 1018,\n",
       "    'contact_rate': {'total': 10180, 'max': 10, 'min': 10, 'mean': 10.0}}}},\n",
       " {'company': {'active': {'count': 1,\n",
       "    'advertising_budget': {'total': 100,\n",
       "     'max': 100,\n",
       "     'min': 100,\n",
       "     'mean': 100.0}}},\n",
       "  'consumer': {'potential': {'count': 57881,\n",
       "    'contact_rate': {'total': 578810, 'max': 10, 'min': 10, 'mean': 10.0}},\n",
       "   'customer': {'count': 2119,\n",
       "    'contact_rate': {'total': 21190, 'max': 10, 'min': 10, 'mean': 10.0}}}},\n",
       " {'company': {'active': {'count': 1,\n",
       "    'advertising_budget': {'total': 100,\n",
       "     'max': 100,\n",
       "     'min': 100,\n",
       "     'mean': 100.0}}},\n",
       "  'consumer': {'potential': {'count': 56717,\n",
       "    'contact_rate': {'total': 567170, 'max': 10, 'min': 10, 'mean': 10.0}},\n",
       "   'customer': {'count': 3283,\n",
       "    'contact_rate': {'total': 32830, 'max': 10, 'min': 10, 'mean': 10.0}}}}]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[customer_acquisition_abm.statistics().get(1.0*key) for key in range(1,5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up Scenarios\n",
    "\n",
    "The scenario mechanism works exactly the same for SD DSL, Agent-based an hybrid models. This ensures that you can keep load different kinds of models and compare results! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BPTK_Py\n",
    "bptk = BPTK_Py.bptk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "customer_acquisition_abm.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "abm_scenario_manager={\n",
    "    \"abm_customer_acquisition\":{\n",
    "        \"name\":\"abm_customer_acquisition\",\n",
    "        \"type\":\"abm\",\n",
    "        \"model\":customer_acquisition_abm,\n",
    "        \"scenarios\":{\n",
    "            \"base\":\n",
    "        {\n",
    "             \"runspecs\": {\n",
    "                  \"starttime\": 1,\n",
    "                  \"stoptime\":60,\n",
    "                  \"dt\": 1.0\n",
    "            },\n",
    "            \"properties\":\n",
    "            {\n",
    "                \"word_of_mouth_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.01\n",
    "                },\n",
    "                \"advertising_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.1\n",
    "                },\n",
    "                \"consumers_reached_per_euro\":\n",
    "                {\n",
    "                    \"type\":\"Integer\",\n",
    "                    \"value\":100\n",
    "                }\n",
    "                \n",
    "            },\n",
    "            \"agents\":\n",
    "            [\n",
    "                {\n",
    "                    \"name\":\"company\",\n",
    "                    \"count\":1,\n",
    "                    \"properties\":{\n",
    "                         \"advertising_budget\":\n",
    "                        {\n",
    "                            \"type\":\"Integer\",\n",
    "                            \"value\":100\n",
    "                        }\n",
    "                    }\n",
    "                },\n",
    "                {\n",
    "                    \"name\":\"consumer\",\n",
    "                    \"count\":60000,\n",
    "                    \"properties\":{\n",
    "                         \"contact_rate\":\n",
    "                         {\n",
    "                            \"type\":\"Integer\",\n",
    "                            \"value\":10\n",
    "                        }\n",
    "                        \n",
    "                    }\n",
    "                }\n",
    "            ]\n",
    "        }\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenario_manager(abm_scenario_manager)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2905eaf3d4594c3195d4c83a10044dcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenario_managers=[\"abm_customer_acquisition\"],\n",
    "    scenarios=[\"base\"],\n",
    "    agents=[\"consumer\"],\n",
    "    agent_states=[\"customer\"],\n",
    "    progress_bar=True\n",
    ")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: especially for larger models it is much better to keep the model and the scenario definitions in separat files, which of course is also possible. See the online [documentation](https://bptk.transentis.com) and  [detailed tutorial](https://github.com/transentis/bptk_py_tutorial) for illustrations of how this can be done. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Best of Both Worlds: Hybrid ABM and SD Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hybrid models are agent based models that \"contain\" a System Dynamics model. The agents can call the elements of the SD model. The SD model elements can also call properties on the agent-based model via user defined sd functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Model\n",
    "from BPTK_Py import sd_functions as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py import Agent\n",
    "from BPTK_Py import Model\n",
    "from BPTK_Py import Event\n",
    "from BPTK_Py import DataCollector\n",
    "from BPTK_Py import SimultaneousScheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Customer(Agent):\n",
    "    def initialize(self):\n",
    "        self.agent_type = \"customer\"\n",
    "        self.state = \"active\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code illustrates how to set up a system dynamics model as a Python class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomerAcquisitionSD():\n",
    "    def __init__(self,model):\n",
    "        self.model = model\n",
    "        \n",
    "        # stocks\n",
    "        self.customers = model.stock(\"customers\")\n",
    "        \n",
    "        #flows\n",
    "        self.customer_acquisition=model.flow(\"customer_acquisition\")\n",
    "        \n",
    "        #converters\n",
    "        self.acquisition_through_advertising = model.converter(\"acquisition_through_advertising\")\n",
    "        self.acquisition_through_word_of_mouth = model.converter(\"acquisition_through_word_of_mouth\")\n",
    "        self.consumers_reached_through_advertising = model.converter(\"consumers_reached_through_advertising\")\n",
    "        self.consumers_reached_through_word_of_mouth= model.converter(\"consumers_reached_through_word_of_mouth\")\n",
    "        self.market_saturation = model.converter(\"market_saturation\")\n",
    "        \n",
    "        #constants\n",
    "        self.initial_customers = model.constant(\"initial_customers\") \n",
    "        self.target_market= model.constant(\"target_market\")\n",
    "        self.advertising_success = model.constant(\"advertising_success\")\n",
    "        self.consumers_reached_per_euro = model.constant(\"consumers_reached_per_ruro\")\n",
    "        self.advertising_budget = model.constant(\"advertising_budget\")\n",
    "        self.word_of_mouth_success = model.constant(\"word_of_mouth_success\")\n",
    "        self.contact_rate = model.constant(\"contact_rate\")\n",
    "        \n",
    "        #equations\n",
    "        self.customers.equation = self.customer_acquisition\n",
    "        self.customer_acquisition.equation=self.acquisition_through_advertising+self.acquisition_through_word_of_mouth\n",
    "        self.acquisition_through_advertising.equation = self.advertising_success*self.consumers_reached_through_advertising\n",
    "        self.consumers_reached_through_advertising.equation = self.consumers_reached_per_euro*self.advertising_budget*(1-self.market_saturation)\n",
    "        self.market_saturation.equation = self.customers/self.target_market\n",
    "        self.acquisition_through_word_of_mouth.equation = self.word_of_mouth_success*self.consumers_reached_through_word_of_mouth\n",
    "        self.consumers_reached_through_word_of_mouth.equation=self.contact_rate*self.customers*(1-self.market_saturation)\n",
    "        \n",
    "        #initialize model\n",
    "        self.customers.initial_value=self.initial_customers\n",
    "        self.initial_customers.equation = 0.0\n",
    "        self.target_market.equation = 60000.0\n",
    "        self.advertising_success.equation = 0.1\n",
    "        self.consumers_reached_per_euro.equation = 1.0\n",
    "        self.advertising_budget.equation = 100.0\n",
    "        self.word_of_mouth_success.equation = 0.01\n",
    "        self.contact_rate.equation = 1.0\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomerAcquisitionHybrid(Model):\n",
    "    def instantiate_model(self):\n",
    "        super().instantiate_model()\n",
    "        # register agent factories\n",
    "        self.register_agent_factory(\"customer\", lambda agent_id,model,properties: Customer(agent_id, model,properties))\n",
    "        \n",
    "        # set up the sd model - keep it in its own class so we can use the SD DSL\n",
    "         \n",
    "        self.sd_model = CustomerAcquisitionSD(self)\n",
    "      \n",
    "    def configure(self,config):\n",
    "        super().configure(config)\n",
    "        \n",
    "        ## the config sets the model properties, which we need to set the initial values of the sd model\n",
    "        self.sd_model.target_market.equation = self.target_market\n",
    "        self.sd_model.advertising_success.equation = self.advertising_success\n",
    "        self.sd_model.consumers_reached_per_euro.equation = self.consumers_reached_per_euro\n",
    "        self.sd_model.advertising_budget.equation = self.advertising_budget\n",
    "        self.sd_model.word_of_mouth_success.equation = self.word_of_mouth_success\n",
    "        self.sd_model.contact_rate.equation = self.contact_rate\n",
    "        \n",
    "        \n",
    "        \n",
    "    def begin_round(self, time, sim_round, step):\n",
    "        # at the beginning of each round we check to see how many customers we should have according to the\n",
    "        # SD model and then create the right number of agents.\n",
    "        \n",
    "        required_num_customers = int(self.evaluate_equation(\"customers\",time))\n",
    "        current_num_customers = self.agent_count(\"customer\")\n",
    "        agents_needed = required_num_customers-current_num_customers\n",
    "        self.create_agents({\"name\":\"customer\",\"count\":agents_needed})\n",
    "        \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid=CustomerAcquisitionHybrid(1,60,dt=1,name=\"Customer Acquisition Hybrid\",scheduler=SimultaneousScheduler(),data_collector=DataCollector())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid.instantiate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid_config =  {\n",
    "             \"runspecs\": {\n",
    "                  \"starttime\": 1,\n",
    "                  \"stoptime\":60,\n",
    "                  \"dt\": 1.0\n",
    "            },\n",
    "            \"properties\":\n",
    "            {\n",
    "                 \"word_of_mouth_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.01\n",
    "                },\n",
    "                \"advertising_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.1\n",
    "                },\n",
    "                \"consumers_reached_per_euro\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":100.0\n",
    "                },\n",
    "                \"advertising_budget\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":100.0\n",
    "                },\n",
    "                \"contact_rate\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":10.0\n",
    "                },\n",
    "                \"target_market\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":60000.0\n",
    "                }\n",
    "                \n",
    "            },\n",
    "            \"agents\":\n",
    "            [\n",
    "                {\n",
    "                    \"name\":\"customer\",\n",
    "                    \"count\":0\n",
    "                }\n",
    "            ]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid.configure(customer_acquisition_hybrid_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'customer': {'active': {'count': 1000}}},\n",
       " {'customer': {'active': {'count': 2081}}},\n",
       " {'customer': {'active': {'count': 3247}}},\n",
       " {'customer': {'active': {'count': 4500}}},\n",
       " {'customer': {'active': {'count': 5842}}},\n",
       " {'customer': {'active': {'count': 7272}}},\n",
       " {'customer': {'active': {'count': 8790}}},\n",
       " {'customer': {'active': {'count': 10393}}}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[customer_acquisition_hybrid.statistics().get(1.0*key) for key in range(2,10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up Scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BPTK_Py\n",
    "bptk = BPTK_Py.bptk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "customer_acquisition_hybrid.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "hybrid_scenario_manager={\n",
    "    \"hybrid_customer_acquisition\":{\n",
    "        \"name\":\"hybrid_customer_acquisition\",\n",
    "        \"type\":\"abm\",\n",
    "        \"model\":customer_acquisition_hybrid,\n",
    "        \"scenarios\":{\n",
    "            \"base\":\n",
    "           {\n",
    "             \"runspecs\": {\n",
    "                  \"starttime\": 1,\n",
    "                  \"stoptime\":60,\n",
    "                  \"dt\": 1.0\n",
    "            },\n",
    "            \"properties\":\n",
    "            {\n",
    "                 \"word_of_mouth_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.01\n",
    "                },\n",
    "                \"advertising_success\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":0.1\n",
    "                },\n",
    "                \"consumers_reached_per_euro\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":100.0\n",
    "                },\n",
    "                \"advertising_budget\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":100.0\n",
    "                },\n",
    "                \"contact_rate\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":10.0\n",
    "                },\n",
    "                \"target_market\":\n",
    "                {\n",
    "                    \"type\":\"Double\",\n",
    "                    \"value\":60000.0\n",
    "                }\n",
    "                \n",
    "            },\n",
    "            \"agents\":\n",
    "            [\n",
    "                {\n",
    "                    \"name\":\"customer\",\n",
    "                    \"count\":0\n",
    "                }\n",
    "                \n",
    "            ]\n",
    "        }\n",
    "        }\n",
    "    }   \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "bptk.register_scenario_manager(hybrid_scenario_manager)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6153411dfac9430087a497ade6171ca8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenario_managers=[\"hybrid_customer_acquisition\"],\n",
    "    scenarios=[\"base\"],\n",
    "    agents=[\"customer\"],\n",
    "    agent_states=[\"active\"],\n",
    "    progress_bar=True\n",
    ")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: especially for larger models it is much better to keep the model and the scenario definitions in separat files, which of course is also possible. See the online [documentation](https://bptk.transentis.com) and  [detailed tutorial](https://github.com/transentis/bptk_py_tutorial) for illustrations of how this can be done. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XMILE Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many modelers prefer to build models using visual modeling environments and not directly in code. One such enviroment is Stella Architect. Using Stella Architect, you can build System Dynamics models visual. You can then import them into Python using BPTK and then work with them much like with any other model.\n",
    "\n",
    "The framework automatically re-transpiles the XMILE model if changes are made in the visual modeling environment. So you can model in Stella Architect and experiment with scenarios in Jupyter.\n",
    "\n",
    "Transpiled models work stand-alone, so you can run them anywhere independently of the modeling environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Up The Model\n",
    "\n",
    "This is what our customer acquisition model looks like in Stella Architect:\n",
    "\n",
    "![Stella Architect](./images/stella_architect.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up The Scenarios\n",
    "\n",
    "To work with XMILE models, all we have to do is set up a [scenario file](./scenarios/scenarios.json) in the `scenarios` folder.\n",
    "\n",
    "The scenario file tells the framework where to find the [XMILE model](./simulation_models/customer_acquisition_xmile.stmx) and which scenarios to set up.\n",
    "\n",
    "In our case the file looks like this - the only difference to the SD DSL scenario definition is the inclusion of the XMILE source.\n",
    "\n",
    "```JSON\n",
    "{\n",
    "    \"xmile_customer_acquisition\":\n",
    "    {\n",
    "        \"source\":\"simulation_models/customer_acquisition_xmile.stmx\",\n",
    "        \"model\":\"simulation_models/customer_acquisition_xmile\",\n",
    "        \"scenarios\":\n",
    "        {\n",
    "          \"base\": { },\n",
    "          \n",
    "        \"low_word_of_mouth\":{\n",
    "            \"constants\":{\n",
    "                \"wordOfMouthSuccess\":0.001\n",
    "            }\n",
    "        },\n",
    "        \"high_word_of_mouth\":{\n",
    "            \"constants\":{\n",
    "                \"wordOfMouthSuccess\":0.1\n",
    "            }\n",
    "        }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As soon as we instantiate bptk, the framework automatically searches the scenarios folder for scenarios and loads them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BPTK_Py\n",
    "bptk = BPTK_Py.bptk()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's list all the scenarios and equations the framework finds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available Equations:\n",
      "\n",
      "Scenario Manager: xmile_customer_acquisition\n",
      "Scenario: base\n",
      "--------------------------\n",
      "\tstock: \t\t\tcustomers\n",
      "\tflow: \t\t\tcustomerAcquisition\n",
      "\tconverter: \t\tacquisitionThroughAdvertising\n",
      "\tconverter: \t\tacquisitionThroughWordOfMouth\n",
      "\tconverter: \t\tadvertisingBudget\n",
      "\tconverter: \t\tadvertisingSuccess\n",
      "\tconverter: \t\tconsumersReachedPerEuro\n",
      "\tconverter: \t\tconsumersReachedThroughAdvertising\n",
      "\tconverter: \t\tconsumersReachedThroughWordOfMouth\n",
      "\tconverter: \t\tcontactRate\n",
      "\tconverter: \t\tinitialCustomers\n",
      "\tconverter: \t\tmarketSaturation\n",
      "\tconverter: \t\ttargetMarket\n",
      "\tconverter: \t\twordOfMouthSuccess\n",
      " \n",
      "Scenario: low_word_of_mouth\n",
      "--------------------------\n",
      "\tstock: \t\t\tcustomers\n",
      "\tflow: \t\t\tcustomerAcquisition\n",
      "\tconverter: \t\tacquisitionThroughAdvertising\n",
      "\tconverter: \t\tacquisitionThroughWordOfMouth\n",
      "\tconverter: \t\tadvertisingBudget\n",
      "\tconverter: \t\tadvertisingSuccess\n",
      "\tconverter: \t\tconsumersReachedPerEuro\n",
      "\tconverter: \t\tconsumersReachedThroughAdvertising\n",
      "\tconverter: \t\tconsumersReachedThroughWordOfMouth\n",
      "\tconverter: \t\tcontactRate\n",
      "\tconverter: \t\tinitialCustomers\n",
      "\tconverter: \t\tmarketSaturation\n",
      "\tconverter: \t\ttargetMarket\n",
      "\tconverter: \t\twordOfMouthSuccess\n",
      " \n",
      "Scenario: high_word_of_mouth\n",
      "--------------------------\n",
      "\tstock: \t\t\tcustomers\n",
      "\tflow: \t\t\tcustomerAcquisition\n",
      "\tconverter: \t\tacquisitionThroughAdvertising\n",
      "\tconverter: \t\tacquisitionThroughWordOfMouth\n",
      "\tconverter: \t\tadvertisingBudget\n",
      "\tconverter: \t\tadvertisingSuccess\n",
      "\tconverter: \t\tconsumersReachedPerEuro\n",
      "\tconverter: \t\tconsumersReachedThroughAdvertising\n",
      "\tconverter: \t\tconsumersReachedThroughWordOfMouth\n",
      "\tconverter: \t\tcontactRate\n",
      "\tconverter: \t\tinitialCustomers\n",
      "\tconverter: \t\tmarketSaturation\n",
      "\tconverter: \t\ttargetMarket\n",
      "\tconverter: \t\twordOfMouthSuccess\n",
      " \n"
     ]
    }
   ],
   "source": [
    "bptk.list_equations(scenario_managers=[\"xmile_customer_acquisition\"],scenarios=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We directly plot the scenarios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bptk.plot_scenarios(\n",
    "    scenario_managers=[\"xmile_customer_acquisition\"],\n",
    "    scenarios=[\"base\",\"high_word_of_mouth\",\"low_word_of_mouth\"],\n",
    "    equations=[\"customers\"],\n",
    "    series_names={\n",
    "                \"xmile_customer_acquisition_base_customers\":\"Base\",\n",
    "                \"xmile_customer_acquisition_low_word_of_mouth_customers\":\"Low Word of Mouth\",\n",
    "                \"xmile_customer_acquisition_high_word_of_mouth_customers\":\"High Word of Mouth\",\n",
    "        }\n",
    ")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: Once the XMILE file has been transpiled into a Python model, the framework is independent of the XMILE file. All simulation is done in Python. The software that was used to create the XMILE file is not needed by the BPTK framework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To learn more about the BPTK framework, you can find extensive [documentation](https://bptk.transentis.com) online and also a [detailed tutorial](https://github.com/transentis/bptk_py_tutorial) on GitHub."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also find more advance examples on GitHub:\n",
    "\n",
    "* [COVID Simulation](https://github.com/transentis/sim-covid-19). Jupyter notebooks and dashboards illustrating the SIR model.\n",
    "* [COVID Simulation Dashboard](https://github.com/transentis/sim-covid-dashboard). A web-based simulation dashboard for the COVID simulation built using our BPTK Widgets library for Javascript. View a [live version](http://www.covid-sim.com) of the dashboard online.\n",
    "* [Beer Distribution Game](https://github.com/transentis/beergame). In-depth analysis of the beergame using both System Dynamics and Agent-based simulation. Includes an illustration of how to use BPTK in conjunction with reinforcement learning to train agents to play the beergame autonomously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get In Touch\n",
    "\n",
    "Please let us know if you need help getting started, if you find a bug or are missing important functionality.\n",
    "\n",
    "A good place to get in touch is our [meetup group](https://www.transentis.com/business-prototyping-toolkit-meetup/en/), that gathers online monthly. This is a convenient setting to see BPTK in action, ask questions and suggest new features.\n",
    "\n",
    "You can also reach us per e-mail at [support@transentis.com](mailto://support@transentis.com)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc"
  },
  "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": 4
}