{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c551f591",
   "metadata": {},
   "source": [
    "# Light Demo\n",
    "\n",
    "This demo shows the simpelst way to create mobility and charging profiles with `CHAMPPY`. Therefore, no paramerization is performed, but existing model parameters are used. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b973d20d",
   "metadata": {},
   "source": [
    "## 1. Import Required Libraries\n",
    "Import all necessary libraries, including pandas and champpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8f96fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import champpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a1cd8",
   "metadata": {},
   "source": [
    "## 2. Check available model parameters\n",
    "\n",
    "The model champpy provides different sets of model parameters, e.g. for different vehicles types. First you have to check which parameters are available using the `ParamsLoader`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b588fa8",
   "metadata": {},
   "outputs": [
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       "      <th>id_params</th>\n",
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       "      <th>temp_res</th>\n",
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       "      <td>Vans based on REM2030 Dataset (https://www.isi...</td>\n",
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       "      <td>Trucks based on Nefton Dataset (https://doi.or...</td>\n",
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       "   id_params                                        description  \\\n",
       "0          1  Vans based on REM2030 Dataset (https://www.isi...   \n",
       "1          2  Trucks based on Nefton Dataset (https://doi.or...   \n",
       "2          3                   Passenger cars based on KID 2010   \n",
       "\n",
       "    vehicle_type  temp_res  annual_km        locations  \\\n",
       "0            Van      0.25  21743.995        [0, 1, 3]   \n",
       "1          Truck      0.25  59064.024  [0, 1, 3, 5, 6]   \n",
       "2  Passenger car      0.25  11568.039        [0, 1, 3]   \n",
       "\n",
       "              share_of_time_at_locations  number_typedays  number_clusters  \\\n",
       "0               [12.411, 58.285, 29.304]                7                1   \n",
       "1  [15.821, 54.941, 15.94, 4.322, 8.976]                7                1   \n",
       "2                [9.588, 18.307, 72.105]                7                1   \n",
       "\n",
       "                                    labels_locations labels_clusters  \\\n",
       "0                   [Driving, Depot, Other location]     [Cluster 1]   \n",
       "1  [Driving, Depot, Other location, Rest area, In...     [Cluster 1]   \n",
       "2            [Driving, Company site, Other location]     [Cluster 1]   \n",
       "\n",
       "  created_user                 created_dt  \n",
       "0  FBiedenbach 2026-03-11 12:28:44.336818  \n",
       "1  FBiedenbach 2026-03-11 12:33:13.254130  \n",
       "2  FBiedenbach 2026-03-11 12:39:39.712594  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate instance of ParamsLoader\n",
    "params_loader= champpy.ParamsLoader()\n",
    "\n",
    "# load info DataFrame\n",
    "params_info_df = params_loader.load_info()\n",
    "params_info_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3cedf5",
   "metadata": {},
   "source": [
    "Select the `id_params` of the model parameters you want to use. In this example, we use parameters for vans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea738930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2026-03-12 11:45:05 - INFO - champpy.core.mobility.parameterization] Load parameters with id_params=1.\n"
     ]
    }
   ],
   "source": [
    "selected_id_params = 1\n",
    "model_params = params_loader.load_params(id_params=selected_id_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981cdc31",
   "metadata": {},
   "source": [
    "## 3. Generate Mobility Profiles\n",
    "Using the `MobModel` and `UserParamsMobModel` to generate synthetic mobility profiles for a specified number of vehicles and date range. The model parameters loaded in the previous step, serves as input to create the instance of `MobModel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b46c2e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2026-03-12 11:46:41 - INFO - champpy.core.mobility.mobility_model] Start generating mobility profiles for 50 vehicles from 2025-01-01 00:00:00 to 2025-12-31 23:00:00\n"
     ]
    },
    {
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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   ],
   "source": [
    "# Generate synthetic mobility profiles\n",
    "mob_model = champpy.MobModel(model_params=model_params)\n",
    "user_params_mob = champpy.UserParamsMobModel(\n",
    "    number_vehicles=50,\n",
    "    start_date=pd.Timestamp(\"2025-01-01-00:00:00\"),\n",
    "    end_date=pd.Timestamp(\"2025-12-31-23:00:00\"),\n",
    ")\n",
    "mob_profiles = mob_model.generate_mob_profiles(user_params=user_params_mob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec22e1f",
   "metadata": {},
   "source": [
    "Display logbook of the generated mobility profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e0d8ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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         "type": "integer"
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         "name": "id_vehicle",
         "rawType": "int64",
         "type": "integer"
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         "name": "dep_dt",
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         "type": "datetime"
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         "name": "dep_loc",
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        {
         "name": "speed",
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       "      <th>id_journey</th>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.50</td>\n",
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      ],
      "text/plain": [
       "   id_journey  id_vehicle              dep_dt              arr_dt  dep_loc  \\\n",
       "0           1           1 2025-01-01 07:00:00 2025-01-01 08:45:00        1   \n",
       "1           2           1 2025-01-01 10:30:00 2025-01-01 11:15:00        3   \n",
       "2           3           1 2025-01-01 11:30:00 2025-01-01 12:00:00        1   \n",
       "3           4           1 2025-01-02 07:30:00 2025-01-02 07:45:00        1   \n",
       "4           5           1 2025-01-02 09:15:00 2025-01-02 09:45:00        1   \n",
       "\n",
       "   arr_loc   distance  duration      speed  \n",
       "0        3  40.738056      1.75  23.278889  \n",
       "1        1  15.549082      0.75  20.732109  \n",
       "2        1   1.205889      0.50   2.411779  \n",
       "3        1   1.420597      0.25   5.682388  \n",
       "4        1   5.628537      0.50  11.257073  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mob_profiles.logbooks.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d96056",
   "metadata": {},
   "source": [
    "## 4. Generate charging profiles\n",
    "Use classes`ChargeModel` and `UserParamsChargeModel` to generate synthetic charging profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdd91cf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2026-03-12 11:46:48 - INFO - champpy.core.mobility.mobility_data] Creating MobArray from MobProfiles\n",
      "[2026-03-12 11:46:48 - INFO - champpy.core.mobility.mobility_data] Extending MobProfiles\n",
      "[2026-03-12 11:46:50 - INFO - root] Generating charging profiles based on mobility data and user parameters...\n"
     ]
    },
    {
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       "Output()"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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   ],
   "source": [
    "# Initilaize the charging model with the modeled mobility prfiles\n",
    "charging_model = champpy.ChargingModel(mob_profiles)\n",
    "\n",
    "# Define user parameters for the charging model\n",
    "user_params_charging = champpy.UserParamsChargingModel(\n",
    "    energy_consumption_kwh_per_km=[0.2],\n",
    "    battery_capacity_kwh=[80.0],\n",
    "    charging_power_max_kw=[11],\n",
    "    efficiency_charging=[0.9],\n",
    "    soc_min=[0.1],\n",
    "    soc_min_dep=[0.8],\n",
    "    soc_initial=1,\n",
    "    distribute_energy_consumption=True,\n",
    "    charging_locations=[1], \n",
    "    temp_res=0.25\n",
    ")\n",
    "\n",
    "# Generate charging profiles based on the mobility profiles and the user parameters for charging\n",
    "charging_profiles = charging_model.generate_charging_profiles(user_params=user_params_charging)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f823e8c9",
   "metadata": {},
   "source": [
    "Display timeseries of the generated charging profiles:"
   ]
  },
  {
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   "execution_count": 8,
   "id": "a6eb68d3",
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       "   id_vehicle            datetime  connected  energy_consumption_kwh  \\\n",
       "0           1 2025-01-01 00:00:00      False                     0.0   \n",
       "1           1 2025-01-01 00:15:00      False                     0.0   \n",
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       "4           1 2025-01-01 01:00:00      False                     0.0   \n",
       "\n",
       "   energy_stored_kwh  power_charging_kw  energy_missing_kwh  \n",
       "0               50.0                0.0                 0.0  \n",
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   "source": [
    "charging_profiles.charging_timeseries.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecf73f1",
   "metadata": {},
   "source": [
    "## 5. Plot mobility and charging profiles\n",
    "\n",
    "You can visulize the modeld mobility and charging profiles using the `MobPlotter` and `ChargingPlotter` classed. They must be initialized by different user parameters defined in the `UserParamsMobPlotter` and `UserParamsChargingPlotter` data classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8741d5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize user parameters for plotting the mobiltiy profiles\n",
    "user_params_plot = champpy.UserParamsMobPlotter(\n",
    "    filename=\"demo01_mobility_profiles_plot.html\",\n",
    "    clustering = False\n",
    ")\n",
    "\n",
    "# Create instance of the mobility plotter\n",
    "mobplot = champpy.MobPlotter(user_params_plot)\n",
    "\n",
    "# Plot the mobility profiles for the merged data (ref + model)\n",
    "mobplot.plot_mob_profiles(mob_profiles)\n",
    "\n",
    "# Initialize user parameters for plotting the charging profiles\n",
    "user_params_plot = champpy.UserParamsChargingPlotter(\n",
    "    filename=\"demo01_charging_profiles_plot.html\",\n",
    "    clustering = False\n",
    ")\n",
    "\n",
    "# Create an instance of the ChargingPlotter\n",
    "chargeplot = champpy.ChargingPlotter(user_params_plot)\n",
    "\n",
    "# Plot the charging profiles\n",
    "chargeplot.plot_charging_profiles(charging_profiles)"
   ]
  }
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