{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f233bd54-261e-497a-9c91-f1b7a8031a70",
   "metadata": {},
   "source": [
    "# Example 9: Travel Time compared to Fluid Age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3ffea4-9cf2-4950-8f5a-b77c4d9d1210",
   "metadata": {},
   "source": [
    "This Example demonstrates how to create a matrix that depicts the travel times between all nodes in a network via a Python algorithm using dataframes provided by PT3S and compares theses results to the fluid age result that is calculated by SIR 3S."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d7f90d",
   "metadata": {},
   "source": [
    "Fluid age is a result calculated for each node in SIR 3S. \n",
    "It describes the amount of time water has spent in the network since leaving a source until reaching a node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e59897-8e57-4f82-857b-a8698a5748c4",
   "metadata": {},
   "source": [
    "# PT3S Release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "931e173f-f062-4a80-a56f-0d96d5c27092",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#pip install PT3S -U --no-deps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf2b2a3-cecb-4679-a0b0-983d2a147e44",
   "metadata": {},
   "source": [
    "# Necessary packages for this Example"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f37aa62b-ae29-42a1-8217-2498db422b36",
   "metadata": {},
   "source": [
    "When running this example for the first time on your machine, please execute the cell below. Afterward, you may need to restart the kernel (using the ‘fast-forward’ button)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0730d039-0023-4545-9295-7e18a8a12dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install - q ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e6fbdc-5dd6-482b-a353-2b6f10e07aab",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59415417-7808-4e32-a2a2-abcde79ff631",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.cm import get_cmap\n",
    "from shapely.geometry import LineString\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import deque\n",
    "from matplotlib.collections import LineCollection\n",
    "import networkx\n",
    "from scipy.sparse import csc_matrix\n",
    "from pandas import Timestamp\n",
    "#...\n",
    "\n",
    "try:\n",
    "    from PT3S import dxAndMxHelperFcts\n",
    "except:\n",
    "    import dxAndMxHelperFcts\n",
    "\n",
    "try:\n",
    "    from PT3S import Rm\n",
    "except:\n",
    "    import Rm\n",
    "\n",
    "try:\n",
    "    from PT3S import ncd\n",
    "except:\n",
    "    import ncd\n",
    "#..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e412b0f-15a9-4d1b-9841-34a10e25b0b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import importlib\n",
    "from importlib import resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ead8811-6d1d-4376-88ba-8ac748284e77",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#importlib.reload(dxAndMxHelperFcts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5882d4-eab8-4da6-8bbe-6776d4195dd2",
   "metadata": {},
   "source": [
    "# Logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "852d5593-37d0-4ccb-be1f-29154bdc142f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = logging.getLogger()  \n",
    "\n",
    "logFileName= r\"Example9.log\" \n",
    "\n",
    "loglevel = logging.DEBUG\n",
    "logging.basicConfig(filename=logFileName\n",
    "                        ,filemode='w'\n",
    "                        ,level=loglevel\n",
    "                        ,format=\"%(asctime)s ; %(name)-60s ; %(levelname)-7s ; %(message)s\")    \n",
    "\n",
    "fileHandler = logging.FileHandler(logFileName)     \n",
    "\n",
    "logger.addHandler(fileHandler)\n",
    "\n",
    "consoleHandler = logging.StreamHandler()\n",
    "consoleHandler.setFormatter(logging.Formatter(\"%(levelname)-7s ; %(message)s\"))\n",
    "consoleHandler.setLevel(logging.INFO)\n",
    "logger.addHandler(consoleHandler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5430ed",
   "metadata": {},
   "source": [
    "# Branched Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8de84e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbFilename=\"Example9_1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d32aabd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbFile = resources.files(\"PT3S\").joinpath(\"Examples\", f\"{dbFilename}.db3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20f2e2d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO    ; Dx.__init__: dbFile (abspath): c:\\users\\aUserName\\3s\\pt3s\\PT3S\\Examples\\Example9_1.db3 exists readable ...\n",
      "INFO    ; Dx.__init__:  SYSTEMKONFIG ID 3 not defined. Value(ID=3) is supposed to define the Model which is used in QGIS. Now QGISmodelXk is undefined ...\n",
      "INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx:  QGISmodelXk not defined. Now the MX of 1st Model in VIEW_MODELLE is used ...\n",
      "INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx: \n",
      "+..\\PT3S\\Examples\\Example9_1.db3 is newer than\n",
      "+..\\PT3S\\Examples\\WDExample7_2\\B1\\V0\\BZ1\\M-1-0-1.1.MX1:\n",
      "+SIR 3S' dbFile is newer than SIR 3S' mx1File\n",
      "+in this case the results are maybe dated or (worse) incompatible to the model\n",
      "INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx: Model is being recalculated using C:\\3S\\SIR 3S\\SirCalc-90-14-02-12_Potsdam.fix1_x64\\SirCalc.exe\n",
      "INFO    ; Mx.setResultsToMxsFile: Mxs: ..\\PT3S\\Examples\\WDExample7_2\\B1\\V0\\BZ1\\M-1-0-1.1.MXS reading ...\n",
      "INFO    ; dxWithMx.__init__: Example9_1: processing dx and mx ...\n"
     ]
    }
   ],
   "source": [
    "m=dxAndMxHelperFcts.readDxAndMx(dbFile=dbFile\n",
    "                                ,preventPklDump=True\n",
    "                                ,maxRecords=-1\n",
    "                                #,SirCalcExePath=r\"C:\\3S\\SIR 3S\\SirCalc-90-14-02-12_Potsdam.fix1_x64\\SirCalc.exe\"\n",
    "                                ,crs=\"EPSG:25832\" # random EPSG\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdc286c",
   "metadata": {},
   "source": [
    "## SIR 3S results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec21d556",
   "metadata": {},
   "source": [
    "### Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "955c7e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfKNOT=m.V3_KNOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90367ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=m.gdf_ROHR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a760ec70",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={'fkKI': 'tki', 'fkKK': 'tkk'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b0370a",
   "metadata": {},
   "source": [
    "#### Fluid age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "343307e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build lookup Series from dfKNOT\n",
    "lookup_TTR = dfKNOT.set_index('pk')['TTR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56a1a936",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to TTR\n",
    "dfROHR['TTR_KI'] = dfROHR['tki'].map(lookup_TTR)\n",
    "dfROHR['TTR_KK'] = dfROHR['tkk'].map(lookup_TTR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67055729",
   "metadata": {},
   "source": [
    "#### Node coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9521261d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_XKOR = dfKNOT.set_index('pk')['XKOR']\n",
    "lookup_YKOR = dfKNOT.set_index('pk')['YKOR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c852c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to XKOR\n",
    "dfROHR['XKOR_KI'] = dfROHR['tki'].map(lookup_XKOR)\n",
    "dfROHR['XKOR_KK'] = dfROHR['tkk'].map(lookup_XKOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "419d3c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to YKOR\n",
    "dfROHR['YKOR_KI'] = dfROHR['tki'].map(lookup_YKOR)\n",
    "dfROHR['YKOR_KK'] = dfROHR['tkk'].map(lookup_YKOR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266b1e4e",
   "metadata": {},
   "source": [
    "#### dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4040f73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=('STAT',\n",
    "  'ROHR~*~*~*~DTTR',\n",
    "  Timestamp('2025-06-05 13:27:46'),\n",
    "  Timestamp('2025-06-05 13:27:46'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1bf7b660",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={dt: 'dt'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36967144",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfROHR['dt']=dfROHR['dt']*3600"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c68e659",
   "metadata": {},
   "source": [
    "#### v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22dc3f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "v=('STAT',\n",
    "  'ROHR~*~*~*~VAV',\n",
    "  Timestamp('2025-06-05 13:27:46'),\n",
    "  Timestamp('2025-06-05 13:27:46'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f0043b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={v:'v'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a31dd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR['dt']=dfROHR['L']/dfROHR['v']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8eb2c1d",
   "metadata": {},
   "source": [
    "#### KVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7552e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR['KVR_KI'] = 1\n",
    "dfROHR['KVR_KK'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4173309f",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca37efb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1654x1169 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, nodes = ncd.plot_ttr_network(\n",
    "    dfROHR,\n",
    "    dn_col=\"DN\",\n",
    "    fk_ki_col='tki',\n",
    "    fk_kk_col='tkk',\n",
    "    geometry_col=\"geometry\",\n",
    "    dt_col='dt',\n",
    "    show_edge_dt=True,\n",
    "    ttr_norm=\"percentile\", ttr_percentiles=(5, 95),\n",
    "    linewidth_range=(7, 15),\n",
    "    node_size=200,\n",
    "    highlight_keys=[5136506604482101815], # source tk                 \n",
    "    highlight_marker_size=250,\n",
    "    show_values=True,\n",
    "    annotation_fmt=\"{:.2f}\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c693abc7",
   "metadata": {},
   "source": [
    "The network has a constant pressure of 100 bar on the left-most node. And equal constant negative volume flow on all right-most nodes. Simulating a source on the left and consumers on the right."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaabee7d",
   "metadata": {},
   "source": [
    "What we can obeserve already here is that the fluid age in the upper branch is lower due to the lower diameter of the upper pipes that leads to a higher velocity and therefore a lower fluid age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2530ea7e",
   "metadata": {},
   "source": [
    "## Comparision: Travel Time Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb86d7c7",
   "metadata": {},
   "source": [
    "### Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d882b591",
   "metadata": {},
   "source": [
    "### Algorithm Implmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "730d6335",
   "metadata": {},
   "outputs": [],
   "source": [
    "TMAX_DETECT = 1.0e12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f805f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_graph(df_data_results_edges):\n",
    "    \"\"\"Setup graph from edges data and results.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_data_results_edges: pandas.DataFrame\n",
    "        Edges data and results.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    G : networkx.Graph\n",
    "        Graph.\n",
    "    \"\"\"\n",
    "\n",
    "    # Nodes: index mapping (here via tk of nodes, alternatively via names of nodes)\n",
    "    list_nodes_tk = sorted(list(set(list(df_data_results_edges[\"tki\"]) + list(df_data_results_edges[\"tkk\"]))))\n",
    "    map_nodes_tk_ind = {list_nodes_tk[ii]: ii for ii in range(len(list_nodes_tk))}\n",
    "\n",
    "    # Adapt DataFrame\n",
    "    df_data_results_edges[\"indi\"] = df_data_results_edges[\"tki\"].apply(lambda x: map_nodes_tk_ind[x])\n",
    "    df_data_results_edges[\"indk\"] = df_data_results_edges[\"tkk\"].apply(lambda x: map_nodes_tk_ind[x])\n",
    "\n",
    "    df_data_results_edges[\"indi_new\"] = df_data_results_edges[[\"indi\", \"indk\"]].min(axis=1)\n",
    "    df_data_results_edges[\"indk_new\"] = df_data_results_edges[[\"indi\", \"indk\"]].max(axis=1)\n",
    "    df_data_results_edges[\"dt_new\"] = np.sign(df_data_results_edges[\"indk\"] - df_data_results_edges[\"indi\"])*df_data_results_edges[\"dt\"]\n",
    "\n",
    "    # Setup graph\n",
    "    G = networkx.Graph()\n",
    "    for _, row in df_data_results_edges.iterrows():\n",
    "        G.add_edge(\n",
    "            row['indi_new'],  # Start node\n",
    "            row['indk_new'],  # End node\n",
    "            # Additional edge attributes\n",
    "            dt=row['dt_new']\n",
    "        )\n",
    "\n",
    "    # Out\n",
    "    return G, map_nodes_tk_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b93a4a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bfs_observedNodes(G, source, tmax):\n",
    "    \"\"\"Breadth-first-search starting at source.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    G : networkx.Graph\n",
    "        Graph.\n",
    "    source : int\n",
    "        Source node.\n",
    "    tmax : float\n",
    "        Maximum travel time.\n",
    "    \"\"\"\n",
    "\n",
    "    visited = set([source])\n",
    "    neighbors = G.neighbors\n",
    "    queue = deque([(source, neighbors(source))])\n",
    "\n",
    "    dt_travel_sum = {source: 0.0}\n",
    "\n",
    "    while queue:\n",
    "        parent, children = queue[0]\n",
    "\n",
    "        try:\n",
    "            child = next(children)\n",
    "\n",
    "            edge = (min(parent, child), max(parent, child))\n",
    "            sign_dt = np.sign(child - parent)\n",
    "\n",
    "            dt_travel = sign_dt*G[edge[0]][edge[1]]['dt']\n",
    "            dt_travel_sum[child] = dt_travel_sum[parent] + np.abs(dt_travel)\n",
    "\n",
    "            if ((child not in visited) and (dt_travel_sum[child] < tmax) and dt_travel < 0.0):\n",
    "                yield parent, child\n",
    "                visited.add(child)\n",
    "                queue.append((child, neighbors(child)))\n",
    "\n",
    "        except StopIteration:\n",
    "            queue.popleft()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "022396e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_travelTimeMatrix(df_data_results_edges):\n",
    "    \"\"\"Setup travel time matrix from edges data and results.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_data_results_edges: pandas.DataFrame\n",
    "        Edges data and results.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    TMat : numpy.ndarray\n",
    "        Travel time matrix.\n",
    "    map_nodes_tk_ind : dict\n",
    "        Mapping nodes tk to index.\n",
    "    \"\"\"\n",
    "\n",
    "    # Setup graph\n",
    "    G, map_nodes_tk_ind = setup_graph(df_data_results_edges)\n",
    "\n",
    "    # Find sets of observed nodes by bfs search and setup matrix V\n",
    "    obsNodes = {}\n",
    "    obsNodesL = {}\n",
    "\n",
    "    travelTimesAccObsNodesDict = {}\n",
    "\n",
    "    for iSens in G.nodes:\n",
    "\n",
    "        bfs_edges = list(bfs_observedNodes(G, iSens, TMAX_DETECT))\n",
    "\n",
    "        nodesSet = set()\n",
    "        endNodeLDict = {}\n",
    "\n",
    "        for (lli, llk) in bfs_edges:\n",
    "            nodesSet.add(lli)\n",
    "            nodesSet.add(llk)\n",
    "            edgex = (min(lli, llk), max(lli, llk))\n",
    "            endNodeLDict[llk] = G[edgex[0]][edgex[1]]['dt']\n",
    "\n",
    "        if (len(nodesSet) > 0):\n",
    "            obsNodes[iSens] = nodesSet - {iSens}\n",
    "            obsNodesL[iSens] = endNodeLDict\n",
    "\n",
    "        # Calculate travel time matrix data\n",
    "        edgesListUse = []\n",
    "        for edgex in bfs_edges:\n",
    "            (ix, kx) = edgex\n",
    "            ix_new, kx_new = min(ix, kx), max(ix, kx)\n",
    "            weight = abs(G[ix_new][kx_new]['dt'])\n",
    "            edgesListUse.append((ix_new, kx_new, weight))\n",
    "\n",
    "        Gtrav = networkx.Graph()\n",
    "        if (len(edgesListUse) > 0):\n",
    "            Gtrav.add_weighted_edges_from(edgesListUse)\n",
    "            travelTimesAccObsNodesDict[iSens] = networkx.single_source_dijkstra_path_length(\n",
    "                Gtrav, source=iSens, cutoff=None, weight='weight')\n",
    "            \n",
    "\n",
    "    # Setup travel time matrix\n",
    "    rowsTTrav = []\n",
    "    columnsTTrav = []\n",
    "    dataTTrav = []\n",
    "\n",
    "    for iSens in obsNodes.keys():\n",
    "        # travel time from node to sensor\n",
    "        ttravs = travelTimesAccObsNodesDict[iSens]\n",
    "        for iObserved in ttravs:\n",
    "            rowsTTrav.append(iSens)\n",
    "            columnsTTrav.append(iObserved)\n",
    "            dataTTrav.append(ttravs[iObserved])\n",
    "\n",
    "    TMatSparse = csc_matrix((dataTTrav, (rowsTTrav, columnsTTrav)), shape=(len(G.nodes), len(G.nodes)))\n",
    "\n",
    "    # Trafo to dense matrices\n",
    "    TMat = 1.0*TMatSparse.toarray()\n",
    "\n",
    "    # Out\n",
    "    return TMat, map_nodes_tk_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d89fe7",
   "metadata": {},
   "source": [
    "### Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5f4fa4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "TMat, map_nodes_tk_ind = setup_travelTimeMatrix(dfROHR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aec45ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "TMat=TMat/3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1f4e3c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 1.53, 0.74, 0.  , 0.  , 0.  , 0.  , 0.49],\n",
       "       [0.  , 0.  , 1.59, 0.  , 0.  , 0.8 , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.8 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 1.53, 0.74, 0.  , 0.  , 0.  , 0.  , 0.49],\n",
       "       [0.  , 0.  , 0.8 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.59, 3.18, 0.  , 0.  , 2.39, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.59, 3.18, 0.  , 0.  , 2.39, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 1.04, 0.25, 0.  , 0.  , 0.  , 0.  , 0.  ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(linewidth=200)\n",
    "TMat.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263a5932",
   "metadata": {},
   "source": [
    "This travel time matrix differs to the fluid age calculated by SIR 3S already in the sense that we can only consider one source at a time. In our given example model this is the case. If we had two sources we would need to consider them (two differnt cols) seperatley."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c51d28f",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b396c563",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1654x1169 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose a source by tk (preferred)\n",
    "source_tk = \"5136506604482101815\"\n",
    "\n",
    "ax, nodes_tt = ncd.plot_travel_time_from_source(\n",
    "    dfROHR,\n",
    "    TMat=TMat,\n",
    "    map_nodes_tk_ind=map_nodes_tk_ind,\n",
    "    source=source_tk,                         # or pass an integer matrix index\n",
    "    cmap=\"viridis\",\n",
    "    linewidth_range=(7, 15),\n",
    "    node_size=200,\n",
    "    ttr_norm=\"percentile\", ttr_percentiles=(5, 95),\n",
    "    treat_zero_as_unreachable=True,           # hides unreachable nodes; source remains at 0.0\n",
    "    highlight_keys=[source_tk],               # star the source (and any others you pass)\n",
    "    highlight_match=\"both\",\n",
    "    colorbar_label=\"Travel time [h]\",\n",
    "    show_axis=False,\n",
    "    show_values=True,\n",
    "    dt_col='dt',\n",
    "    show_edge_dt=True,\n",
    "    annotation_fmt=\"{:.2f}\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f18e06",
   "metadata": {},
   "source": [
    "If we compare this plot to the first one, that used the fluid age calculated by SIR 3S directley, we observe that they are identical."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e412a1cb",
   "metadata": {},
   "source": [
    "# Meshed Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d6d3c4",
   "metadata": {},
   "source": [
    "The model 9_2 differs to 9_1 only in that the upper and lower branch are connected via a pipe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b34591a-c2d1-486b-b853-ed38cd5c8323",
   "metadata": {},
   "source": [
    "## Read Model and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29b18b2c-f2df-4b27-a96c-0e03af6f16b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbFilename=\"Example9_2\"\n",
    "dbFile=os.path.join(os.path.dirname(os.path.abspath(dxAndMxHelperFcts.__file__))\n",
    "                    +'/Examples/'\n",
    "                    +dbFilename\n",
    "                    +'.db3'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f8b5221c-0fd1-4fbc-abaf-81325ad2629d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO    ; Dx.__init__: dbFile (abspath): c:\\users\\aUserName\\3s\\pt3s\\PT3S\\Examples\\Example9_2.db3 exists readable ...\n",
      "INFO    ; Dx.__init__:  SYSTEMKONFIG ID 3 not defined. Value(ID=3) is supposed to define the Model which is used in QGIS. Now QGISmodelXk is undefined ...\n",
      "INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx:  QGISmodelXk not defined. Now the MX of 1st Model in VIEW_MODELLE is used ...\n",
      "INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx: Model is being recalculated using C:\\3S\\SIR 3S\\SirCalc-90-14-02-12_Potsdam.fix1_x64\\SirCalc.exe\n",
      "INFO    ; Mx.setResultsToMxsFile: Mxs: ..\\PT3S\\Examples\\WDExample9_2\\B1\\V0\\BZ1\\M-1-0-1.1.MXS reading ...\n",
      "INFO    ; dxWithMx.__init__: Example9_2: processing dx and mx ...\n"
     ]
    }
   ],
   "source": [
    "m=dxAndMxHelperFcts.readDxAndMx(dbFile=dbFile\n",
    "                                ,preventPklDump=True\n",
    "                                ,maxRecords=-1\n",
    "                                #,SirCalcExePath=r\"C:\\3S\\SIR 3S\\SirCalc-90-14-02-12_Potsdam.fix1_x64\\SirCalc.exe\"\n",
    "                                ,crs=\"EPSG:25832\" # random EPSG\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac81bf4",
   "metadata": {},
   "source": [
    "## SIR 3S results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6ec354",
   "metadata": {},
   "source": [
    "### Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "525cbdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfKNOT=m.V3_KNOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cf17f907",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=m.gdf_ROHR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfc23fd",
   "metadata": {},
   "source": [
    "#### TTR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bcd9442f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build lookup Series from dfKNOT\n",
    "lookup_TTR = dfKNOT.set_index('pk')['TTR']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5351a31d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to TTR\n",
    "dfROHR['TTR_KI'] = dfROHR['fkKI'].map(lookup_TTR)\n",
    "dfROHR['TTR_KK'] = dfROHR['fkKK'].map(lookup_TTR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae27f13d",
   "metadata": {},
   "source": [
    "#### Node coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "448ef6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_XKOR = dfKNOT.set_index('pk')['XKOR']\n",
    "lookup_YKOR = dfKNOT.set_index('pk')['YKOR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e98db88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to XKOR\n",
    "dfROHR['XKOR_KI'] = dfROHR['fkKI'].map(lookup_XKOR)\n",
    "dfROHR['XKOR_KK'] = dfROHR['fkKK'].map(lookup_XKOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b260a11c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map fkKI and fkKK to YKOR\n",
    "dfROHR['YKOR_KI'] = dfROHR['fkKI'].map(lookup_YKOR)\n",
    "dfROHR['YKOR_KK'] = dfROHR['fkKK'].map(lookup_YKOR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72f059a",
   "metadata": {},
   "source": [
    "#### KVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d8aa4b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR['KVR_KI'] = 1\n",
    "dfROHR['KVR_KK'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9c67df",
   "metadata": {},
   "source": [
    "#### dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c45544c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_abs=('STAT',\n",
    "  'ROHR~*~*~*~DTTR',\n",
    "  Timestamp('2025-06-05 13:27:46'),\n",
    "  Timestamp('2025-06-05 13:27:46'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cd01fae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={dt_abs: 'dt_abs'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "10057b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR['dt_abs']=dfROHR['dt_abs']*3600"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0735e821",
   "metadata": {},
   "source": [
    "#### v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "79e8f5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "v=('STAT',\n",
    "  'ROHR~*~*~*~VAV',\n",
    "  Timestamp('2025-06-05 13:27:46'),\n",
    "  Timestamp('2025-06-05 13:27:46'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9c48fb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={v:'v'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5b5bddec",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR['dt']=dfROHR['L']/dfROHR['v']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf36fc1",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "233da37b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1654x1169 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, nodes = ncd.plot_ttr_network(\n",
    "    dfROHR,\n",
    "    dn_col=\"DN\",\n",
    "    fk_ki_col='fkKI',\n",
    "    fk_kk_col='fkKK',\n",
    "    geometry_col=\"geometry\",\n",
    "    ttr_norm=\"percentile\", ttr_percentiles=(5, 95),\n",
    "    linewidth_range=(7, 15),\n",
    "    node_size=200,\n",
    "    highlight_keys=[5136506604482101815], # source tk                 \n",
    "    highlight_marker_size=250,\n",
    "    show_values=True,\n",
    "    dt_col='dt',\n",
    "    show_edge_dt=True,\n",
    "    annotation_fmt=\"{:.2f}\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee670cef",
   "metadata": {},
   "source": [
    "We can observe that due to the inserted connection between the upper and lower branch the travel time in the upper branch changes, because water flows from the lower to the upper branch."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bf151f",
   "metadata": {},
   "source": [
    "Why does the node in the upper branch that is connected to the lower branch have a water age of 2.39 h?\n",
    "This node receives water from two pipes, therefore its water age is the water age coming from both pipes weighted by the volume flow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d535ff36",
   "metadata": {},
   "source": [
    "Example Calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a04bf687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     93.552284\n",
       "1    100.000000\n",
       "2    100.000000\n",
       "3    306.447723\n",
       "4    306.447723\n",
       "5    100.000000\n",
       "6    100.000000\n",
       "7     93.552284\n",
       "8    106.447716\n",
       "Name: QMAVAbs, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfROHR['QMAVAbs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9948c3b6",
   "metadata": {},
   "source": [
    "The pipe reaching the node from the left has a volume flow of 94 m^3/h\n",
    "The pipe reaching the node from the bottom has a volume flow of 106 m^3/h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f42c2b",
   "metadata": {},
   "source": [
    "Fluid age = (94^3/h * (1.70h+0.52h) + 106^3/h * (1.04h+1.49h)) / (94^3/h + 106^3/h) = 2.39h (approx due to rounding error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155bb643",
   "metadata": {},
   "source": [
    "This approach also works for nodes not supplied 100% by one source."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7db70c3",
   "metadata": {},
   "source": [
    "## Comparision: Travel Time Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "906dea8c",
   "metadata": {},
   "source": [
    "### Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "370604c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfROHR=dfROHR.rename(columns={'fkKI': 'tki', 'fkKK': 'tkk'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b957334",
   "metadata": {},
   "source": [
    "### Algorithm Implmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadce5f0",
   "metadata": {},
   "source": [
    "We reuse the algorithm already implemented"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f85376",
   "metadata": {},
   "source": [
    "### Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0f689cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "TMat, map_nodes_tk_ind = setup_travelTimeMatrix(dfROHR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5d87a40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TMat=TMat/3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0c6eb0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 1.98, 2.72, 1.02, 0.  , 2.5 , 0.  , 0.  , 0.49],\n",
       "       [0.  , 0.  , 1.04, 0.  , 0.  , 0.52, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 1.7 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.98, 2.72, 1.02, 0.  , 2.5 , 0.  , 0.  , 0.49],\n",
       "       [0.  , 0.  , 0.52, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.59, 2.63, 0.  , 0.  , 2.11, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.59, 2.63, 0.  , 0.  , 2.11, 0.  , 0.  , 0.  ],\n",
       "       [0.  , 1.49, 2.22, 0.52, 0.  , 2.01, 0.  , 0.  , 0.  ]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(linewidth=200)\n",
    "TMat.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51526ec4",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "755c1b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1654x1169 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose a source by tk (preferred)\n",
    "source_tk = \"5136506604482101815\"\n",
    "\n",
    "ax, nodes_tt = ncd.plot_travel_time_from_source(\n",
    "    dfROHR,\n",
    "    TMat=TMat,\n",
    "    map_nodes_tk_ind=map_nodes_tk_ind,\n",
    "    source=source_tk,                         # or pass an integer matrix index\n",
    "    cmap=\"viridis\",\n",
    "    linewidth_range=(7, 15),\n",
    "    node_size=200,\n",
    "    ttr_norm=\"percentile\", ttr_percentiles=(5, 95),\n",
    "    treat_zero_as_unreachable=True,           # hides unreachable nodes; source remains at 0.0\n",
    "    highlight_keys=[source_tk],               # star the source (and any others you pass)\n",
    "    highlight_match=\"both\",\n",
    "    colorbar_label=\"Travel time [h]\",\n",
    "    show_axis=False,\n",
    "    show_values=True,\n",
    "    dt_col='dt',\n",
    "    show_edge_dt=True,\n",
    "    annotation_fmt=\"{:.2f}\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c1ee50",
   "metadata": {},
   "source": [
    "If we compare this plot to the previous one, that used the fluid age calculated by SIR 3S directley, we observe that they the travel time values do not equal the fluid age. The reason for that is that the travel time algorithm uses a Dijkstra algorithm on a graph that has edges that are weighted by the time spent in each pipe. If we now consider the node connected to four pipes in the upper branch. We see that the travel time is lower than the fluid age calculated. As the travel time algorithm does not consider flow this is not unexpected. The travel time algorithm only considers the upper branch for the calculation of the travel time and not the contribution of the lower branch."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
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