diff --git a/epyt/tutorials/Basic_functionality.ipynb b/epyt/tutorials/Basic_functionality.ipynb
index c40a060..51480fb 100644
--- a/epyt/tutorials/Basic_functionality.ipynb
+++ b/epyt/tutorials/Basic_functionality.ipynb
@@ -62,20 +62,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b382605-7614-4c97-9b8d-112db55eefc0",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-06-20T09:02:19.219738400Z",
-     "start_time": "2024-06-20T09:02:18.838840Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "id": "464f61f7-7ae5-41d5-a5b8-9c467b890c93",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install epyt"
+    "%pip install epyt==1.1.8"
    ]
   },
   {
@@ -211,7 +202,7 @@
     }
    },
    "source": [
-    "![Alt text](images/firstpic.jpg)\n"
+    "![Alt text](images/epyt_tree.png)"
    ]
   },
   {
@@ -229,6 +220,16 @@
     "Just type \"help\" followed by the command\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33b3a878-9bc6-4808-8ce8-fcd097ff561a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(G.getLinkDiameter)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -245,7 +246,6 @@
    },
    "outputs": [],
    "source": [
-    "help(G.getLinkDiameter)\n",
     "help(G.setNodeBaseDemands)"
    ]
   },
@@ -278,20 +278,9 @@
    },
    "outputs": [],
    "source": [
-    "# write \"G.\" and press \"Tab\" key to find a command"
+    "# Write \"G.\" and press \"Tab\" key to find a command"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "c09ccf9cc60a4d3c",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "d415f0c0acab549b",
@@ -323,29 +312,21 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import display, clear_output\n",
-    "\n",
     "# Assuming G has a plot method\n",
     "# Simple plot\n",
-    "G.plot()\n",
-    "\n",
-    "# Highlight specific nodes\n",
-    "G.plot(highlightnode=['n616', 'n281', 'n31'])\n",
-    "display(plt.gcf())\n",
-    "plt.close()\n"
+    "G.plot()"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "222c59d52510b9e0",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": []
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2506fe6a-d43f-4dfd-986c-a6d839d9e601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Highlight specific nodes\n",
+    "G.plot(highlightnode=['n616', 'n281', 'n31'])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -357,7 +338,7 @@
     }
    },
    "source": [
-    "## Getting information from links (pipes, pumps, valves)\n",
+    "### Getting information from links (pipes, pumps, valves)\n",
     "\n",
     "\n",
     "![Alt text](images/statistics.jpeg)\n"
@@ -425,8 +406,16 @@
     "print(\"diameters = \", diameters)\n",
     "\n",
     "pipelength = G.getLinkLength([1,2,3])\n",
-    "print(\"pipelength = \", pipelength)\n",
-    "\n",
+    "print(\"pipelength = \", pipelength)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa5fabe0-9bfc-4a1c-8c5d-b70a4039a5e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "LinkNameID = G.getLinkNameID()\n",
     "print(\"LinkNameID = \", LinkNameID)\n",
     "\n",
@@ -531,7 +520,7 @@
    },
    "outputs": [],
    "source": [
-    "## Try it yourself below"
+    "# Try it yourself below"
    ]
   },
   {
@@ -566,11 +555,27 @@
    "outputs": [],
    "source": [
     "demandcategories = G.getNodeDemandCategoriesNumber()\n",
-    "print(\"demandcategories = \", demandcategories)\n",
-    "\n",
+    "print(\"demandcategories = \", demandcategories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "929f3b2f-33b7-42ff-ad41-fd51f1979e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "demands = G.getNodeBaseDemands()\n",
-    "print(\"demands = \", demands)\n",
-    "\n",
+    "print(\"demands = \", demands)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65400228-880d-492e-8925-eb36688ef439",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "demandscategory1 = G.getNodeBaseDemands(1) # Get categories 1\n",
     "print(\"demandscategory1 = \", demandscategory1)\n",
     "\n",
@@ -612,15 +617,17 @@
    },
    "outputs": [],
    "source": [
-    "#change pipe diameter\n",
-    "d4 = G.getLinkDiameter(4)\n",
+    "# Change pipe diameter\n",
+    "link_index = 4\n",
+    "link_index_py = link_index - 1\n",
+    "d4 = G.getLinkDiameter(link_index)\n",
     "print(\"d4 = \", d4)\n",
     "\n",
     "diameters = G.getLinkDiameter()\n",
     "print(\"diameters = \", diameters)\n",
     "\n",
-    "diameters[4 - 1] = 200 #python starts from 0 \n",
-    "print(\"diameters[4] = \",diameters[4 - 1])\n",
+    "diameters[link_index_py] = 200 # Starts from 0 \n",
+    "print(\"diameters[4] = \",diameters[link_index_py])\n",
     "\n",
     "G.setLinkDiameter(diameters)\n",
     "d4new = G.getLinkDiameter(4)\n",
@@ -656,18 +663,20 @@
    },
    "outputs": [],
    "source": [
-    "#change node elevation\n",
-    "e4 = G.getNodeElevations(4)\n",
+    "# Change node elevation\n",
+    "node_index = 4\n",
+    "node_index_py = node_index - 1\n",
+    "e4 = G.getNodeElevations(node_index)\n",
     "print(\"e4 = \",e4)\n",
     "\n",
     "elevations = G.getNodeElevations()\n",
     "print(\"elevations = \", elevations)\n",
     "\n",
-    "elevations[4] = 70\n",
-    "print(\"elevations[4]  = \", elevations[4])\n",
+    "elevations[node_index_py] = 70\n",
+    "print(\"elevations[4]  = \", elevations[node_index_py])\n",
     "G.setNodeElevations(elevations)\n",
     "\n",
-    "e4new = G.getNodeElevations(5)\n",
+    "e4new = G.getNodeElevations(node_index)\n",
     "print(\"e4new = \", e4new)"
    ]
   },
@@ -743,7 +752,6 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
     "# Create a new figure\n",
     "fig, ax = plt.subplots()\n",
     "fig.set_figwidth(7)\n",
@@ -754,7 +762,6 @@
     "ax.set_ylabel(f'Pattern', fontsize=8)\n",
     "# Turn on the grid\n",
     "plt.grid(True)\n",
-    "\n",
     "# Show the plot\n",
     "plt.show()"
    ]
@@ -809,7 +816,6 @@
     "fig.set_figwidth(7)\n",
     "# Plot the first row of Patterns\n",
     "plt.plot(Patterns[patternIndex-1, :])\n",
-    "\n",
     "ax.set_title('Residential pattern', fontsize=8)\n",
     "ax.set_xlabel('Time', fontsize=8)\n",
     "ax.set_ylabel(f'Pattern', fontsize=8)\n",
@@ -829,6 +835,7 @@
     "### Simulation options\n",
     "\n",
     "Description and code for simulation options.\n",
+    "\n",
     "![Alt text](images/simulationoption.jpeg)"
    ]
   },
@@ -848,16 +855,17 @@
    },
    "outputs": [],
    "source": [
-    "SimulationDuration = G.getTimeSimulationDuration() #in seconds\n",
+    "# Times in seconds\n",
+    "SimulationDuration = G.getTimeSimulationDuration() \n",
     "print(\"SimulationDuration = \", SimulationDuration)\n",
     "\n",
     "SimulationDurationHours = SimulationDuration/3600\n",
     "print(\"SimulationDurationHours = \", SimulationDurationHours)\n",
     "\n",
-    "HydraulicStep = G.getTimeHydraulicStep() #in seconds\n",
+    "HydraulicStep = G.getTimeHydraulicStep() \n",
     "print(\"HydraulicStep = \", HydraulicStep)\n",
     "\n",
-    "QualityStep = G.getTimeQualityStep() #in seconds\n",
+    "QualityStep = G.getTimeQualityStep()\n",
     "print(\"QualityStep = \", QualityStep)"
    ]
   },
@@ -877,10 +885,10 @@
    },
    "outputs": [],
    "source": [
-    "PatternStep = G.getTimePatternStep() #in seconds\n",
+    "PatternStep = G.getTimePatternStep() \n",
     "print(\"PatternStep = \", PatternStep)\n",
     "\n",
-    "ReportingStep = G.getTimeReportingStep() #in seconds\n",
+    "ReportingStep = G.getTimeReportingStep()\n",
     "print(\"ReportingStep = \", ReportingStep)\n",
     "\n",
     "ReportingStart = G.getTimeReportingStart()\n",
@@ -903,6 +911,17 @@
     "### Hydraulic options\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb959b71-67fe-4341-8d8f-4b8cefbe9e02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Units = G.getUnits()\n",
+    "print(\"Units = \", G.getAllAttributes(Units))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -919,13 +938,10 @@
    },
    "outputs": [],
    "source": [
-    "Units = G.getUnits()\n",
-    "print(\"Units = \", Units)\n",
-    "\n",
     "FlowUnits = G.getFlowUnits()\n",
     "print(\"FlowUnits = \", FlowUnits)\n",
     "\n",
-    "PressureUnits = G.NodePressureUnits\n",
+    "PressureUnits = Units.NodePressureUnits\n",
     "print(\"PressureUnits = \", PressureUnits)\n",
     "\n",
     "HeadLossFormula = G.getOptionsHeadLossFormula()\n",
@@ -965,11 +981,22 @@
     "\n",
     "zeroNodes = np.zeros(G.NodeCount)\n",
     "print(\"zeroNodes = \", zeroNodes)\n",
-    "G.setNodeInitialQuality(zeroNodes) #setting initial chlorine level at 0  mg/L\n",
     "\n",
+    "# Setting initial chlorine level at 0  mg/L\n",
+    "G.setNodeInitialQuality(zeroNodes) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8173138-9e9f-4fdc-abbd-a5614ea6f176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "brk = -0.3 * np.ones(G.LinkCount)\n",
     "print(\"brk = \", brk)\n",
-    "G.setLinkBulkReactionCoeff(brk) #setting Bulk Reaction Coefficient"
+    "# Setting Bulk Reaction Coefficient\n",
+    "G.setLinkBulkReactionCoeff(brk) "
    ]
   },
   {
@@ -990,7 +1017,8 @@
    "source": [
     "wrk = -0.1 * np.ones(G.LinkCount)\n",
     "print(\"wrk = \", wrk)\n",
-    "G.setLinkWallReactionCoeff(wrk) #setting Pipe wall Reaction Coefficient\n",
+    "# Setting Pipe wall Reaction Coefficient\n",
+    "G.setLinkWallReactionCoeff(wrk)\n",
     "\n",
     "BulkReactionOrder = G.getOptionsPipeBulkReactionOrder()\n",
     "print(\"BulkReactionOrder = \", BulkReactionOrder)\n",
@@ -1035,9 +1063,16 @@
     "\n",
     "for i in ReservoirsIndex:\n",
     "    G.setNodeSourceType(i, 'CONCEN')\n",
-    "    G.setNodeSourceQuality(i, 0.8)\n",
-    "\n",
-    "\n",
+    "    G.setNodeSourceQuality(i, 0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42791c53-665a-40e0-ba15-69b47c971e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "NodeSourceQuality = G.getNodeSourceQuality(ReservoirsIndex)\n",
     "print(\"NodeSourceQuality = \", NodeSourceQuality)"
    ]
diff --git a/epyt/tutorials/Run_First_EPANET_Analysis.ipynb b/epyt/tutorials/Run_First_EPANET_Analysis.ipynb
index 103bff3..6425eb7 100644
--- a/epyt/tutorials/Run_First_EPANET_Analysis.ipynb
+++ b/epyt/tutorials/Run_First_EPANET_Analysis.ipynb
@@ -2,84 +2,117 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "f5e4a1a0ab4123ff",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
-    "# EpyT Basic Functionalities\n",
+    "# Basic Usage of EPyT\n",
     "\n",
-    "### Table of Contents\n",
-    "* Initialize EPANET-MATLAB Toolkit\n",
+    "### Outline\n",
+    "* Initialize EPANET-Python Toolkit\n",
     "* Running your first analysis\n",
-    "* Other methods  to run EPANET\n",
+    "* Other method to run EPANET\n",
     "* Plot results\n",
     "* Iteratively (step-by-step) hydraulic analysis\n",
     "* Iteratively (step-by-step) quality analysis"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "f5e4a1a0ab4123ff"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "e66af3efe802e0d3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Initialize EPANET Python Toolkit (EPyT)\n",
     "\n",
     "You should always begin with this command to import the toolkit."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "e66af3efe802e0d3"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "af472bf6-58b9-4102-af4c-7de5dd1fe940",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from epyt import epanet"
-   ],
+    "%pip install epyt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32dc981b7df58ac2",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:42.530048800Z",
      "start_time": "2024-06-20T09:30:42.467316100Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "32dc981b7df58ac2",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "from epyt import epanet"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "9ab75467312bfab3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Load a benchmark network\n",
     "Decide which benchmark network to use and load the network"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "9ab75467312bfab3"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "a19a6512998d48e8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T09:30:43.034397800Z",
+     "start_time": "2024-06-20T09:30:42.554090700Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "#decide which network to load from the \"/networks/\" folder  \n",
-    "filename = 'Net1.inp' #you can also try 'net2-cl2.inp', 'Net3.inp', etc.\n",
+    "# Decide which network to load from the \"/networks/\" folder  \n",
+    "filename = 'net2-cl2.inp' # You can also try 'net2-cl2.inp', 'Net3.inp', etc.\n",
     "\n",
-    "#call epanet class and load all data and functions in G structure\n",
+    "# Call epanet class and load all data and functions in G structure\n",
     "G = epanet(filename)\n",
     "G.plot()"
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf16e38e375972f",
    "metadata": {
     "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-20T09:30:43.034397800Z",
-     "start_time": "2024-06-20T09:30:42.554090700Z"
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "a19a6512998d48e8",
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
    "source": [
     "### Running your first analysis\n",
     "There are 4 ways to run a simulation using the Toolkit:\n",
@@ -89,195 +122,236 @@
     "3.     Using the ENepanet function binary file\n",
     "4.     Using the command line and the binary file\n",
     "\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "4bf16e38e375972f"
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "#set simulation duration at hours \n",
-    "hrs = 48 \n",
-    "G.setTimeSimulationDuration(3600*hrs)"
-   ],
+   "execution_count": null,
+   "id": "fc39b8c3f2ce3639",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:43.034397800Z",
      "start_time": "2024-06-20T09:30:42.943540200Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "fc39b8c3f2ce3639",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "# Set simulation duration at hours \n",
+    "hrs = 48 \n",
+    "G.setTimeSimulationDuration(3600*hrs)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Execute the simulation using the batch hydraulic analysis method\n"
-   ],
+   "id": "f2fa44cf59171470",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "f2fa44cf59171470"
+   "source": [
+    "### Execute the simulation using the batch hydraulic analysis method\n"
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "H = G.getComputedHydraulicTimeSeries()\n",
-    "G.getAllAttributes(H)"
-   ],
+   "execution_count": null,
+   "id": "b9d405f2d6ba9df7",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:43.170461300Z",
      "start_time": "2024-06-20T09:30:42.958055700Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "b9d405f2d6ba9df7",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "H = G.getComputedHydraulicTimeSeries()\n",
+    "G.getAllAttributes(H)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Execute the simulation using the batch quality analysis method"
-   ],
+   "id": "b605d50d766968db",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "b605d50d766968db"
+   "source": [
+    "### Execute the simulation using the batch quality analysis method"
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "Q = G.getComputedQualityTimeSeries()\n",
-    "G.getAllAttributes(Q)"
-   ],
+   "execution_count": null,
+   "id": "696f64a1dab4d583",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:43.230144100Z",
      "start_time": "2024-06-20T09:30:43.058687200Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "696f64a1dab4d583",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "Q = G.getComputedQualityTimeSeries()\n",
+    "G.getAllAttributes(Q)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Other methods  to run EPANET"
-   ],
+   "id": "b41c10e23d6de09c",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "b41c10e23d6de09c"
+   "source": [
+    "### Other methods  to run EPANET"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Method using the EPANET executable"
-   ],
+   "id": "58230995827c03ff",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "58230995827c03ff"
+   "source": [
+    "#### Method using the EPANET executable"
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "Hexe = G.getComputedTimeSeries()\n",
-    "G.getAllAttributes(Hexe)"
-   ],
+   "execution_count": null,
+   "id": "aa79e0a6158ebb8b",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:43.632487300Z",
      "start_time": "2024-06-20T09:30:43.118354400Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "aa79e0a6158ebb8b",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "Hexe = G.getComputedTimeSeries()\n",
+    "G.getAllAttributes(Hexe)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Method using the ENepanet library function (fastest)"
-   ],
+   "id": "6b72020f3f42ebac",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "6b72020f3f42ebac"
+   "source": [
+    "### Method using the ENepanet library function"
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "Hbatch = G.getComputedTimeSeries_ENepanet()\n",
-    "G.getAllAttributes(Hbatch)"
-   ],
+   "execution_count": null,
+   "id": "c9a3380ec9ad43b",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-06-20T09:30:43.718249300Z",
      "start_time": "2024-06-20T09:30:43.626860900Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "c9a3380ec9ad43b",
-   "execution_count": null
+   "outputs": [],
+   "source": [
+    "Hbatch = G.getComputedTimeSeries_ENepanet()\n",
+    "G.getAllAttributes(Hbatch)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Plot results"
-   ],
+   "id": "e2aed1aed9876191",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "e2aed1aed9876191"
+   "source": [
+    "### Plot results"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "1bc3158238fd647b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T09:30:43.764685500Z",
+     "start_time": "2024-06-20T09:30:43.723949500Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "import datetime\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "#Get pressure in meters\n",
+    "# Get pressure in meters\n",
     "P = H.Pressure\n",
-    "print(P)\n",
-    "\n",
+    "G.printv(P)\n",
     "F = H.Flow\n",
-    "print(F)\n",
-    "\n",
+    "G.printv(F)\n",
     "C = Q.NodeQuality\n",
-    "print(C)\n",
-    "#Your turn: Plot Chlorine in node with index 10 "
-   ],
+    "G.printv(C)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff44fc37f178697",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-06-20T09:30:43.764685500Z",
-     "start_time": "2024-06-20T09:30:43.723949500Z"
+     "end_time": "2024-06-20T09:31:30.592780900Z",
+     "start_time": "2024-06-20T09:31:27.653674500Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "1bc3158238fd647b",
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
    "outputs": [],
    "source": [
     "T = H.Time.astype(float)  \n",
     "Tcl = Q.Time.astype(float)  \n",
-    "TD = [datetime.datetime(2022, 7, 18, 0, 0) + datetime.timedelta(seconds=int(t)) for t in T]  \n",
-    "TQ = [datetime.datetime(2022, 7, 18, 0, 0) + datetime.timedelta(seconds=int(t)) for t in Tcl] \n",
+    "TD = [datetime.datetime(2024, 7, 1, 0, 0) + datetime.timedelta(seconds=int(t)) for t in T]  \n",
+    "TQ = [datetime.datetime(2024, 7, 1, 0, 0) + datetime.timedelta(seconds=int(t)) for t in Tcl] \n",
     "\n",
     "plt.rc('xtick', labelsize=5)\n",
     "plt.rc('ytick', labelsize=7)\n",
@@ -290,13 +364,12 @@
     "ax.set_ylabel(\"Pressure (meters)\", fontsize=7)\n",
     "ax.set_xlabel(\"Time\", fontsize=7)\n",
     "\n",
-    "#pipeindex = G.getLinkIndex(['0'])\n",
+    "pipeindex = G.getLinkIndex('1')\n",
     "fig, ax = plt.subplots(figsize=(4, 3))\n",
     "fig.set_figwidth(8)\n",
     "ax.plot(TD, F[:, pipeindex])\n",
     "ax.grid(True)\n",
-    "#ax.set_title(f\"Flow in Pipe {G.getLinkNameID(pipeindex)}\", fontsize=8)\n",
-    "ax.set_title(f\"Flow in Pipe \", fontsize=8)\n",
+    "ax.set_title(f\"Flow in Pipe {G.getLinkNameID(pipeindex)}\", fontsize=8)\n",
     "ax.set_ylabel(\"Flow (CMH)\", fontsize=7)\n",
     "ax.set_xlabel(\"Time\", fontsize=7)\n",
     "\n",
@@ -308,97 +381,123 @@
     "ax.set_ylabel(\"Free Chlorine (mg/L)\", fontsize=7)\n",
     "ax.set_xlabel(\"Time\", fontsize=7)\n",
     "\n",
-    "plt.show()"
-   ],
+    "plt.show()\n",
+    "\n",
+    "# Your turn: Plot Chlorine in node with index 10 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf0a386bfff73b43",
    "metadata": {
     "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-20T09:31:30.592780900Z",
-     "start_time": "2024-06-20T09:31:27.653674500Z"
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "6ff44fc37f178697",
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
    "source": [
     "![Alt text](images/pic1.jpeg)"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "bf0a386bfff73b43"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Plot node pressures for specific nodes "
-   ],
+   "id": "326a65e8704563af",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "326a65e8704563af"
+   "source": [
+    "### Plot node pressures for specific nodes "
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "303d7ef88d9cc6ee",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-06-20T09:30:44.461322400Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
-    "node_indices = [1,2,3] #nodes 1, 2 and 3 \n",
+    "node_indices = [1,2,3] \n",
     "print(\"Node_indeces = \",node_indices)\n",
-    "node_names = [G.getNodeNameID(idx) for idx in node_indices]  # Python indexing starts from 0\n",
+    "node_names = [G.getNodeNameID(idx) for idx in node_indices]\n",
     "Units = G.getUnits()\n",
     "FlowUnits = G.getFlowUnits()\n",
-    "print(\"Flow_Units = \",FlowUnits)\n",
+    "print(\"Flow_Units = \", FlowUnits)\n",
     "\n",
-    "fig, axs = plt.subplots(3, 1, figsize=(8, 6), sharex=True) \n",
+    "fig, axs = plt.subplots(3, 1, figsize=(8, 6)) \n",
     "\n",
     "for k, node_index in enumerate(node_indices):\n",
     "    ax = axs[k] \n",
-    "    ax.plot(TD, P[:, node_index - 1])  # Python indexing starts from 0\n",
+    "    node_index_py = node_index - 1 # Python indexing starts from 0\n",
     "    fig.set_figwidth(7)\n",
-    "    ax.set_title(f'Pressure for the node id \"{node_names[k]}\"', fontsize=11)\n",
+    "    ax.plot(TD, P[:, node_index_py])\n",
+    "    ax.grid(True)\n",
+    "    ax.set_title(f'Pressure for the node id \"{node_names[k]}\"', fontsize=8)\n",
+    "    ax.set_ylabel(f'Pressure ({G.NodePressureUnits})', fontsize=7) \n",
     "    ax.set_xlabel('Time (hrs)', fontsize=7)\n",
-    "    ax.set_ylabel(f'Pressure ({G.NodePressureUnits})', fontsize=9) \n",
     "\n",
+    "plt.tight_layout() # Automatically adjusts spacing to fit the subplots\n",
     "plt.show()"
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f871aec8fb701b4",
    "metadata": {
     "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-06-20T09:30:44.461322400Z"
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "303d7ef88d9cc6ee",
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
    "source": [
     "![Alt text](images/pic2.jpeg)\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "6f871aec8fb701b4"
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Iteratively (step-by-step) hydraulic analysis"
-   ],
+   "id": "c68e1622a247b31b",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "c68e1622a247b31b"
+   "source": [
+    "### Iteratively (step-by-step) hydraulic analysis"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "9fc26765279b1afb",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-06-20T09:30:44.464117400Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "# 4 steps to run hydraulics\n",
-    "G.openHydraulicAnalysis()  # Step 1\n",
-    "G.initializeHydraulicAnalysis()  # Step 2\n",
+    "\n",
+    "# Step 1\n",
+    "G.openHydraulicAnalysis()  \n",
+    "\n",
+    "# Step 2\n",
+    "G.initializeHydraulicAnalysis() \n",
     "\n",
     "tstep = 1\n",
     "P2 = []\n",
@@ -408,76 +507,105 @@
     "F2 = []\n",
     "\n",
     "while tstep > 0:\n",
-    "    ttmp = G.runHydraulicAnalysis()  # Step 3\n",
+    "    \n",
+    "    # Step 3\n",
+    "    ttmp = G.runHydraulicAnalysis()  \n",
     "    P2.append(G.getNodePressure())\n",
     "    D2.append(G.getNodeActualDemand())\n",
     "    H2.append(G.getNodeElevations())\n",
     "    #H2.append(G.getNodeHydaulicHead())\n",
     "    F2.append(G.getLinkFlows())\n",
     "    T2.append(ttmp)\n",
-    "    tstep = G.nextHydraulicAnalysisStep()  # Step 4\n",
+    "    \n",
+    "    # Step 4\n",
+    "    tstep = G.nextHydraulicAnalysisStep()  \n",
     "\n",
     "G.closeHydraulicAnalysis()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-06-20T09:30:44.464117400Z"
-    }
-   },
-   "id": "9fc26765279b1afb",
-   "execution_count": null
+   ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0961169f-91ae-4976-a9c4-31f187ab7bfb",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "### Plot figures"
-   ],
+    "# Print pressures P2\n",
+    "print(P2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8728521d95df08fe",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "8728521d95df08fe"
+   "source": [
+    "#### Plot figures"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "5218adb452387e86",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-06-20T09:30:44.465860Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
-    "#import matplotlib.pyplot as plt\n",
+    "TD = [datetime.datetime(2024, 7, 1, 0, 0) + datetime.timedelta(seconds=int(t)) for t in T2]  \n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "for i, node_index in enumerate(node_indices):\n",
-    "    ax.plot(TD, [row[node_index - 1] for row in P2], label=f'Node {node_index}')\n",
-    "ax.set_title(f'Pressure for 3 nodes ', fontsize=10)\n",
-    "ax.set_xlabel('Time', fontsize=8)\n",
+    "    node_index_py = node_index - 1\n",
+    "    ax.plot(TD, [row[node_index_py] for row in P2], label=f'Node {node_index}')\n",
+    "ax.set_title(f'Pressure for first 3 nodes ', fontsize=10)\n",
+    "ax.set_xlabel('Time (seconds)', fontsize=9)\n",
     "ax.set_ylabel(f'Pressure({G.NodePressureUnits})', fontsize=8)\n",
     "fig.set_figwidth(7)\n",
     "ax.legend(loc='upper right', fontsize=7)\n",
     "plt.show()"
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f0900f6cfded80f",
    "metadata": {
     "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-06-20T09:30:44.465860Z"
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "5218adb452387e86",
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
    "source": [
     "### Iteratively (step-by-step) quality analysis"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "2f0900f6cfded80f"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "659f766bd479d64",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-06-20T09:30:44.468038400Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
-    "# requires solveCompleteHydraulics\n",
+    "# Requires solveCompleteHydraulics - ignored because we run hydraulic analysis beforehand.\n",
+    "\n",
     "G.openQualityAnalysis()\n",
     "G.initializeQualityAnalysis()\n",
     "tleft = 1\n",
@@ -493,28 +621,34 @@
     "    tleft = G.stepQualityAnalysisTimeLeft()\n",
     "\n",
     "G.closeQualityAnalysis()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-06-20T09:30:44.468038400Z"
-    }
-   },
-   "id": "659f766bd479d64",
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "### Plot figures"
-   ],
+   "id": "6ed7b9f7a9f860e5",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "id": "6ed7b9f7a9f860e5"
+   "source": [
+    "#### Plot figures"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "ac2a65193b177584",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-06-20T09:30:44.470424Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
@@ -526,47 +660,42 @@
     "\n",
     "fig.set_figwidth(6)\n",
     "plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2024-06-20T09:30:44.470424Z"
-    }
-   },
-   "id": "ac2a65193b177584",
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [],
+   "execution_count": null,
+   "id": "86de5a2a71f2e85d",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "start_time": "2024-06-20T09:30:44.472728800Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "86de5a2a71f2e85d",
-   "execution_count": null
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
+   "display_name": "Python 3 (Spyder)",
+   "language": "python3",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
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