{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5556432f",
   "metadata": {},
   "source": [
    "# Example 2: Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8899e69a",
   "metadata": {},
   "source": [
    "This Example demonstrates the capabilities of the class Dataframes_SIR3S_Model that extends SIR3S_Model be abilities to work directley with pandas dataframes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d40c36",
   "metadata": {},
   "source": [
    "# Toolkit Release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fb5a07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6773e12",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134689cf",
   "metadata": {},
   "source": [
    "## SIR 3S Toolkit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69665f68",
   "metadata": {},
   "source": [
    "### Regular Import/Init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79d0fb2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIR3S_SIRGRAF_DIR = r\"C:\\3S\\SIR 3S Entwicklung\\SirGraf-90-15-00-19_x64\" #change to local path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f41d17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sir3stoolkit.core import wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d539c981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'sir3stoolkit.core.wrapper' from 'C:\\\\Users\\\\aUsername\\\\3S\\\\sir3stoolkit\\\\src\\\\sir3stoolkit\\\\core\\\\wrapper.py'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37d59df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf2c56e",
   "metadata": {},
   "source": [
    "### Additional Import/Init for Dataframes class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea003338",
   "metadata": {},
   "source": [
    "from sir3stoolkit.mantle import dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08da19eb",
   "metadata": {},
   "source": [
    "We can just import the whole mantle to get all higher level modeling functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "893f889e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sir3stoolkit.mantle import mantle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b65297f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n"
     ]
    }
   ],
   "source": [
    "s3s = mantle.Mantle_SIR3S_Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d814ca8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sir3stoolkit.mantle import mantle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "567bcae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n"
     ]
    }
   ],
   "source": [
    "s3s = mantle.Mantle_SIR3S_Model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2007993a",
   "metadata": {},
   "source": [
    "## Additional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a443734",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from shapely.geometry import Point\n",
    "import re\n",
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from shapely import wkt\n",
    "import matplotlib.pyplot as plt\n",
    "import contextily as cx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a78e4d",
   "metadata": {},
   "source": [
    "# Open Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20ff3c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model is open for further operation\n"
     ]
    }
   ],
   "source": [
    "s3s.OpenModel(dbName=r\"C:\\Users\\aUsername\\3S\\PT3S\\PT3S\\Examples\\Example3.db3\",\n              providerType=s3s.ProviderTypes.SQLite,\n              Mid=\"M-1-0-1\",\n              saveCurrentlyOpenModel=False,\n              namedInstance=\"\",\n              userID=\"\",\n              password=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1297b929",
   "metadata": {},
   "source": [
    "# Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6af11f5",
   "metadata": {},
   "source": [
    "## District Heating Consumer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eed41b9",
   "metadata": {},
   "source": [
    "### Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c60f6cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-10-17 13:22:33,659] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.DistrictHeatingConsumer\n",
      "[2025-10-17 13:22:33,667] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 337 element(s) of element type ObjectTypes.DistrictHeatingConsumer.\n",
      "[2025-10-17 13:22:33,691] DEBUG in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 0 metadata properties.\n",
      "[2025-10-17 13:22:33,692] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving geometry...\n",
      "[2025-10-17 13:22:33,724] INFO in sir3stoolkit.mantle.dataframes: [metadata] 0 non-empty end node columns were created)\n",
      "[2025-10-17 13:22:33,726] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (337, 2)\n"
     ]
    }
   ],
   "source": [
    "df_consumer_metadata=s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.DistrictHeatingConsumer, properties=[], geometry=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08c6c586",
   "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>tk</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4743997951091160959</td>\n",
       "      <td>POINT (713181.847468032 5578489.9250612)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5014209100699808035</td>\n",
       "      <td>POINT (713369.326806115 5578395.12330167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4627580049017248376</td>\n",
       "      <td>POINT (713251.536184149 5578455.25247764)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tk                                   geometry\n",
       "0  4743997951091160959   POINT (713181.847468032 5578489.9250612)\n",
       "1  5014209100699808035  POINT (713369.326806115 5578395.12330167)\n",
       "2  4627580049017248376  POINT (713251.536184149 5578455.25247764)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_consumer_metadata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de616e8",
   "metadata": {},
   "source": [
    "### Result Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90e7e467",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-10-17 13:22:33,759] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.DistrictHeatingConsumer\n",
      "[2025-10-17 13:22:33,760] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] No timestamps were given. Checking available simulation timestamps (SIR3S_Model.GetTimeStamps()).\n",
      "[2025-10-17 13:22:33,868] INFO in sir3stoolkit.mantle.dataframes: 25 simulation timestamps will be used.\n",
      "[2025-10-17 13:22:33,938] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 25 valid timestamps will be used.\n",
      "[2025-10-17 13:22:33,938] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieved 337 tks.\n",
      "[2025-10-17 13:22:33,942] INFO in sir3stoolkit.mantle.dataframes: [results] Using 1 result properties.\n",
      "[2025-10-17 13:22:33,943] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...\n",
      "[2025-10-17 13:22:34,704] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (8425, 3)\n"
     ]
    }
   ],
   "source": [
    "df_consumer_results=s3s.generate_element_results_dataframe(s3s.ObjectTypes.DistrictHeatingConsumer, [\"W\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e8db479",
   "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>timestamp</th>\n",
       "      <th>tk</th>\n",
       "      <th>W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>4743997951091160959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>5014209100699808035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>4627580049017248376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        timestamp                   tk  W\n",
       "0  2023-02-13 00:00:00.000 +01:00  4743997951091160959  0\n",
       "1  2023-02-13 00:00:00.000 +01:00  5014209100699808035  0\n",
       "2  2023-02-13 00:00:00.000 +01:00  4627580049017248376  0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_consumer_results.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49290dad",
   "metadata": {},
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13956e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_consumer = pd.merge(\n",
    "    df_consumer_metadata,\n",
    "    df_consumer_results,\n",
    "    on=\"tk\",\n",
    "    how=\"outer\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5f3d755",
   "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>tk</th>\n",
       "      <th>geometry</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4611752310942477664</td>\n",
       "      <td>POINT (713675.30023234 5578705.1927566)</td>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>39.3675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4611752310942477664</td>\n",
       "      <td>POINT (713675.30023234 5578705.1927566)</td>\n",
       "      <td>2023-02-13 01:00:00.000 +01:00</td>\n",
       "      <td>39.83363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4611752310942477664</td>\n",
       "      <td>POINT (713675.30023234 5578705.1927566)</td>\n",
       "      <td>2023-02-13 02:00:00.000 +01:00</td>\n",
       "      <td>40.48209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tk                                 geometry  \\\n",
       "0  4611752310942477664  POINT (713675.30023234 5578705.1927566)   \n",
       "1  4611752310942477664  POINT (713675.30023234 5578705.1927566)   \n",
       "2  4611752310942477664  POINT (713675.30023234 5578705.1927566)   \n",
       "\n",
       "                        timestamp         W  \n",
       "0  2023-02-13 00:00:00.000 +01:00   39.3675  \n",
       "1  2023-02-13 01:00:00.000 +01:00  39.83363  \n",
       "2  2023-02-13 02:00:00.000 +01:00  40.48209  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_consumer.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d494e629",
   "metadata": {},
   "source": [
    "## Pipes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02644004",
   "metadata": {},
   "source": [
    "### Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d4c590a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-10-17 13:22:34,775] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Pipe\n",
      "[2025-10-17 13:22:34,784] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n",
      "[2025-10-17 13:22:34,786] DEBUG in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 2 metadata properties.\n",
      "[2025-10-17 13:22:34,787] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving metadata properties ['DN', 'Kvr'], geometry, end nodes...\n",
      "[2025-10-17 13:22:34,979] INFO in sir3stoolkit.mantle.dataframes: [metadata] 2 non-empty end node columns were created)\n",
      "[2025-10-17 13:22:34,979] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (524, 6)\n"
     ]
    }
   ],
   "source": [
    "df_pipes_metadata=s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Pipe, properties=[\"DN\", \"Kvr\"], geometry=True, end_nodes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "411c7eb0",
   "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>tk</th>\n",
       "      <th>DN</th>\n",
       "      <th>Kvr</th>\n",
       "      <th>geometry</th>\n",
       "      <th>fkKI</th>\n",
       "      <th>fkKK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5442010239090746007</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713620.267807079 5578828.41875394,...</td>\n",
       "      <td>5669301360686511351</td>\n",
       "      <td>5397948523091900401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4917786378639043296</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713602.294599536 5578860.10567987,...</td>\n",
       "      <td>5397948523091900401</td>\n",
       "      <td>5239335112004772156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4762482310382009633</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713574.061626605 5578909.87290685,...</td>\n",
       "      <td>5239335112004772156</td>\n",
       "      <td>5298886695042021307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tk   DN Kvr  \\\n",
       "0  5442010239090746007  150   1   \n",
       "1  4917786378639043296  125   1   \n",
       "2  4762482310382009633  125   1   \n",
       "\n",
       "                                            geometry                 fkKI  \\\n",
       "0  LINESTRING (713620.267807079 5578828.41875394,...  5669301360686511351   \n",
       "1  LINESTRING (713602.294599536 5578860.10567987,...  5397948523091900401   \n",
       "2  LINESTRING (713574.061626605 5578909.87290685,...  5239335112004772156   \n",
       "\n",
       "                  fkKK  \n",
       "0  5397948523091900401  \n",
       "1  5239335112004772156  \n",
       "2  5298886695042021307  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pipes_metadata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef579444",
   "metadata": {},
   "source": [
    "### Result data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "333d9c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-10-17 13:22:35,016] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe\n",
      "[2025-10-17 13:22:35,018] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] No timestamps were given. Checking available simulation timestamps (SIR3S_Model.GetTimeStamps()).\n",
      "[2025-10-17 13:22:35,189] INFO in sir3stoolkit.mantle.dataframes: 25 simulation timestamps will be used.\n",
      "[2025-10-17 13:22:35,287] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 25 valid timestamps will be used.\n",
      "[2025-10-17 13:22:35,315] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieved 524 tks.\n",
      "[2025-10-17 13:22:35,319] INFO in sir3stoolkit.mantle.dataframes: [results] Using 1 result properties.\n",
      "[2025-10-17 13:22:35,319] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...\n",
      "[2025-10-17 13:22:37,094] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (13100, 3)\n"
     ]
    }
   ],
   "source": [
    "df_pipes_results=s3s.generate_element_results_dataframe(s3s.ObjectTypes.Pipe, [\"QMAV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f24edb04",
   "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>timestamp</th>\n",
       "      <th>tk</th>\n",
       "      <th>QMAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>5442010239090746007</td>\n",
       "      <td>52.84819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>4917786378639043296</td>\n",
       "      <td>29.67386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>4762482310382009633</td>\n",
       "      <td>30.2523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        timestamp                   tk      QMAV\n",
       "0  2023-02-13 00:00:00.000 +01:00  5442010239090746007  52.84819\n",
       "1  2023-02-13 00:00:00.000 +01:00  4917786378639043296  29.67386\n",
       "2  2023-02-13 00:00:00.000 +01:00  4762482310382009633   30.2523"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pipes_results.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a98578cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes_results[\"QMAV\"]=df_pipes_results[\"QMAV\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce63b98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes_results[\"QMAV\"]=df_pipes_results[\"QMAV\"].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0708539c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes_results=df_pipes_results.rename(columns={\"QMAV\": \"QMAVAbs\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a014cac",
   "metadata": {},
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49f5df10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes = pd.merge(\n",
    "    df_pipes_metadata,\n",
    "    df_pipes_results,\n",
    "    on=\"tk\",\n",
    "    how=\"outer\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "977bc8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes=df_pipes[df_pipes[\"Kvr\"] == \"1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b038e191",
   "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>tk</th>\n",
       "      <th>DN</th>\n",
       "      <th>Kvr</th>\n",
       "      <th>geometry</th>\n",
       "      <th>fkKI</th>\n",
       "      <th>fkKK</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>QMAVAbs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4615723899944629797</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713738.296567236 5579219.90221333,...</td>\n",
       "      <td>5129584372458662150</td>\n",
       "      <td>5332825919690090061</td>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>3.667083e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4615723899944629797</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713738.296567236 5579219.90221333,...</td>\n",
       "      <td>5129584372458662150</td>\n",
       "      <td>5332825919690090061</td>\n",
       "      <td>2023-02-13 01:00:00.000 +01:00</td>\n",
       "      <td>1.440640e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4615723899944629797</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713738.296567236 5579219.90221333,...</td>\n",
       "      <td>5129584372458662150</td>\n",
       "      <td>5332825919690090061</td>\n",
       "      <td>2023-02-13 02:00:00.000 +01:00</td>\n",
       "      <td>1.414446e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tk   DN Kvr  \\\n",
       "25  4615723899944629797  999   1   \n",
       "26  4615723899944629797  999   1   \n",
       "27  4615723899944629797  999   1   \n",
       "\n",
       "                                             geometry                 fkKI  \\\n",
       "25  LINESTRING (713738.296567236 5579219.90221333,...  5129584372458662150   \n",
       "26  LINESTRING (713738.296567236 5579219.90221333,...  5129584372458662150   \n",
       "27  LINESTRING (713738.296567236 5579219.90221333,...  5129584372458662150   \n",
       "\n",
       "                   fkKK                       timestamp       QMAVAbs  \n",
       "25  5332825919690090061  2023-02-13 00:00:00.000 +01:00  3.667083e-10  \n",
       "26  5332825919690090061  2023-02-13 01:00:00.000 +01:00  1.440640e-09  \n",
       "27  5332825919690090061  2023-02-13 02:00:00.000 +01:00  1.414446e-09  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pipes.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d94dcf9",
   "metadata": {},
   "source": [
    "### Plotting Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9fbad0d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_point_string(s):\n",
    "    # Extract numbers from the string using regex\n",
    "    match = re.match(r'POINT\\s*\\(\\s*([-\\d\\.]+)\\s+([-\\d\\.]+)\\s*\\)', s)\n",
    "    if match:\n",
    "        x, y = float(match.group(1)), float(match.group(2))\n",
    "        return Point(x, y)\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8c4e2229",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_consumer['geometry'] = df_consumer['geometry'].apply(parse_point_string)\n",
    "\n",
    "x_mean = df_consumer['geometry'].apply(lambda p: p.x if p else None).mean()\n",
    "y_mean = df_consumer['geometry'].apply(lambda p: p.y if p else None).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd0dbcd",
   "metadata": {},
   "source": [
    "### Change datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "641a4835",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_pipes['DN'] = pd.to_numeric(df_pipes['DN'], errors='coerce')\n",
    "df_pipes['QMAVAbs'] = pd.to_numeric(df_pipes['QMAVAbs'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f12a79",
   "metadata": {},
   "source": [
    "### Filter rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "078de081",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes = df_pipes[df_pipes['DN'] != 999]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4ba784",
   "metadata": {},
   "source": [
    "### Turn into gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8766534c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pipes['geometry'] = df_pipes['geometry'].apply(wkt.loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3b207191",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_pipes = gpd.GeoDataFrame(df_pipes, geometry='geometry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9ff7006",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_pipes=gdf_pipes.set_crs(epsg=4326, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3b5b2a7c",
   "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>tk</th>\n",
       "      <th>DN</th>\n",
       "      <th>Kvr</th>\n",
       "      <th>geometry</th>\n",
       "      <th>fkKI</th>\n",
       "      <th>fkKK</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>QMAVAbs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4621904482639719098</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713283.93228 5578718.12301, 713299...</td>\n",
       "      <td>5106194195554624313</td>\n",
       "      <td>5416743601805578486</td>\n",
       "      <td>2023-02-13 00:00:00.000 +01:00</td>\n",
       "      <td>86.89403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4621904482639719098</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713283.93228 5578718.12301, 713299...</td>\n",
       "      <td>5106194195554624313</td>\n",
       "      <td>5416743601805578486</td>\n",
       "      <td>2023-02-13 01:00:00.000 +01:00</td>\n",
       "      <td>87.91040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>4621904482639719098</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (713283.93228 5578718.12301, 713299...</td>\n",
       "      <td>5106194195554624313</td>\n",
       "      <td>5416743601805578486</td>\n",
       "      <td>2023-02-13 02:00:00.000 +01:00</td>\n",
       "      <td>89.32416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tk   DN Kvr  \\\n",
       "75  4621904482639719098  200   1   \n",
       "76  4621904482639719098  200   1   \n",
       "77  4621904482639719098  200   1   \n",
       "\n",
       "                                             geometry                 fkKI  \\\n",
       "75  LINESTRING (713283.93228 5578718.12301, 713299...  5106194195554624313   \n",
       "76  LINESTRING (713283.93228 5578718.12301, 713299...  5106194195554624313   \n",
       "77  LINESTRING (713283.93228 5578718.12301, 713299...  5106194195554624313   \n",
       "\n",
       "                   fkKK                       timestamp   QMAVAbs  \n",
       "75  5416743601805578486  2023-02-13 00:00:00.000 +01:00  86.89403  \n",
       "76  5416743601805578486  2023-02-13 01:00:00.000 +01:00  87.91040  \n",
       "77  5416743601805578486  2023-02-13 02:00:00.000 +01:00  89.32416  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_pipes.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1504ac",
   "metadata": {},
   "source": [
    "# Plotting (WORK IN PROGRESS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bdf20e4",
   "metadata": {},
   "source": [
    "The function used below is not yet released."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "73f88ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1169.29x1653.54 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=s3s.din_figsize(\"A3\", orientation=\"portrait\"))\n",
    "\n",
    "\n",
    "# 1) Create the first layer (no color mixing)\n",
    "layer1 = s3s.create_pipe_layer(\n",
    "    gdf=gdf_pipes,\n",
    "    geometry_col=\"geometry\",\n",
    "    cmap=\"viridis\",\n",
    "    width_scaling_col=\"DN\",\n",
    "    #color_mixing_col=\"QMAVAbs\",\n",
    "    min_width = 10,\n",
    "    max_width = 30,\n",
    "    default_color = (0.5, 0.5, 0.5, 1)\n",
    ")\n",
    "\n",
    "ax = layer1[\"axes\"]  # grab axes from the first layer\n",
    "\n",
    "layer2 = s3s.create_pipe_layer(\n",
    "    gdf=gdf_pipes,\n",
    "    ax=ax,\n",
    "    geometry_col=\"geometry\",\n",
    "    cmap=\"viridis\",\n",
    "    width_scaling_col=\"QMAVAbs\",\n",
    "    color_mixing_col=\"QMAVAbs\",\n",
    "    min_width = 5,\n",
    "    max_width = 10,\n",
    ")\n",
    "\n",
    "layer1[\"artist\"].set_zorder(1)  \n",
    "layer2[\"artist\"].set_zorder(10)  \n",
    "\n",
    "ax.set_title(\"Network Pipes\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}