epyt_leakage_simulation_and_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5b4d80c8378e6208",
      "metadata": {
        "id": "5b4d80c8378e6208"
      },
      "source": [
        "## Leakage Simulation and Detection"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "13de5d565e358b71",
      "metadata": {
        "id": "13de5d565e358b71"
      },
      "source": [
        "### Initialize EPANET Python Toolkit (EPyT)\n",
        "\n",
        "You should always begin with this command to import the toolkit.\n",
        "\n",
        "[EPyT](https://github.com/OpenWaterAnalytics/EPyT) is available on [PyPI](https://pypi.org/project/epyt/) and can be installed via `pip install epyt`. To upgrade to the latest version if it's already installed, use `pip install --upgrade epyt`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 219,
      "id": "3092f609-265d-44ec-877c-bcb7d7f03190",
      "metadata": {
        "id": "3092f609-265d-44ec-877c-bcb7d7f03190",
        "outputId": "39d76d71-8f00-4bdb-80aa-e8b1f24d5d4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: epyt in /usr/local/lib/python3.10/dist-packages (1.1.9)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from epyt) (1.25.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from epyt) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from epyt) (2.0.3)\n",
            "Requirement already satisfied: xlsxwriter in /usr/local/lib/python3.10/dist-packages (from epyt) (3.2.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from epyt) (67.7.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->epyt) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->epyt) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->epyt) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->epyt) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "%pip install epyt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 220,
      "id": "8e6dec8e9e9b12ff",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:53.382274Z",
          "start_time": "2024-06-19T09:02:48.789183Z"
        },
        "id": "8e6dec8e9e9b12ff",
        "outputId": "3ab16fd5-34b6-49c0-e0b5-fca9ed2ab51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "from epyt import epanet\n",
        "import tempfile\n",
        "import requests\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ce2cc492c4061fea",
      "metadata": {
        "id": "ce2cc492c4061fea"
      },
      "source": [
        "<img src=\"https://github.com/WaterFutures/EPyT-and-EPyT-Flow-Tutorial/blob/main/icons/large_leakage.png?raw=1\" alt=\"Large Leakage\" style=\"display: inline-block; width: 50%; height: 50%;\">\n",
        "<img src=\"https://github.com/WaterFutures/EPyT-and-EPyT-Flow-Tutorial/blob/main/icons/fire-fighting.png?raw=1\" alt=\"Fire-Fighting\" style=\"display: inline-block; width: 50%; height: 50%;\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c3395e7f1cb58e4",
      "metadata": {
        "id": "5c3395e7f1cb58e4"
      },
      "source": [
        "### How pressure changes during large leakages and fire-fighting"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f91b00fad751a5eb",
      "metadata": {
        "id": "f91b00fad751a5eb"
      },
      "source": [
        "When flow is increased within the network, e.g., due to fire-fighting, the pressure changes near that location. Investigate this change, at `node '32'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 221,
      "id": "f29cce4bdd1856b2",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:53.523944Z",
          "start_time": "2024-06-19T09:02:53.385478Z"
        },
        "id": "f29cce4bdd1856b2",
        "outputId": "251769da-3de0-44b8-fe64-1bfe1a65404f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File downloaded and saved as /tmp/Net1.inp\n"
          ]
        }
      ],
      "source": [
        "def download_file(url, file_path):\n",
        "    \"\"\"\n",
        "    Downloads a file from the given URL and saves it to the specified file path.\n",
        "\n",
        "    Parameters:\n",
        "    url (str): The URL to download the file from.\n",
        "    file_path (str): The path where the downloaded file should be saved.\n",
        "\n",
        "    Returns:\n",
        "    str: A message indicating the result of the download.\n",
        "    \"\"\"\n",
        "    try:\n",
        "        response = requests.get(url)\n",
        "        response.raise_for_status()  # Check if the request was successful\n",
        "        with open(file_path, 'wb') as file:\n",
        "            file.write(response.content)\n",
        "        return f\"File downloaded and saved as {file_path}\"\n",
        "    except requests.exceptions.HTTPError as err:\n",
        "        return f\"HTTP error occurred: {err}\"\n",
        "    except Exception as err:\n",
        "        return f\"An error occurred: {err}\"\n",
        "\n",
        "# Download Net1.inp\n",
        "url = \"https://raw.githubusercontent.com/WaterFutures/EPyT-and-EPyT-Flow-Tutorial/main/data/Net1.inp\"\n",
        "tmpdirpath = tempfile.gettempdir()\n",
        "inpname = os.path.join(tmpdirpath, \"Net1.inp\")\n",
        "result = download_file(url, inpname)\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 222,
      "id": "affb1894-671f-43ae-a57b-150feebfadee",
      "metadata": {
        "id": "affb1894-671f-43ae-a57b-150feebfadee",
        "outputId": "3102bee6-3796-487a-b452-0ef6dbc4229a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPANET version 20200 loaded (EPyT version v1.1.9 - Last Update: 24/06/2024).\n",
            "Input File Net1.inp loaded successfully.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "G = epanet(inpname)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "id": "8ecb2495e0b87de7",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.185186Z",
          "start_time": "2024-06-19T09:02:53.525949Z"
        },
        "id": "8ecb2495e0b87de7",
        "outputId": "c54b8ab2-cdc0-4413-eed7-b34a693cc350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "nodeID = '32'\n",
        "G.plot(highlightnode=nodeID)\n",
        "G.plot_close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a1301bd92846b8a",
      "metadata": {
        "id": "a1301bd92846b8a"
      },
      "source": [
        "#### During fire-fighting, the base demand may increase up to x5. Will there be sufficient pressure?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 224,
      "id": "8740a2342b267956",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.194081Z",
          "start_time": "2024-06-19T09:02:54.187305Z"
        },
        "id": "8740a2342b267956",
        "outputId": "72e512fb-b2cb-4e1d-dd60-5b8b2338131d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "idx = 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "idx = G.getNodeIndex(nodeID)\n",
        "G.printv(idx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 225,
      "id": "822d1137bf20dc85",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.208358Z",
          "start_time": "2024-06-19T09:02:54.199564Z"
        },
        "id": "822d1137bf20dc85",
        "outputId": "32fcae7e-ee6c-4c18-b130-5472df16a6f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bd = [100.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "bd = G.getNodeBaseDemands(idx)[1]\n",
        "G.printv(bd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "id": "cce26b2acc081bf5",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.221722Z",
          "start_time": "2024-06-19T09:02:54.212473Z"
        },
        "id": "cce26b2acc081bf5",
        "outputId": "66facea0-330d-4f21-a6f3-7b7f5215a433",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "# Demand multiplicator\n",
        "demand_multi = 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "id": "f4ad76ac5d455dce",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.235822Z",
          "start_time": "2024-06-19T09:02:54.225251Z"
        },
        "id": "f4ad76ac5d455dce",
        "outputId": "9d48ee55-6357-4a81-ddd2-efa6cb831be2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "d32 = [100. 200. 300. 400. 500. 600. 700. 800.]\n",
            "Help on built-in function arange in module numpy:\n",
            "\n",
            "arange(...)\n",
            "    arange([start,] stop[, step,], dtype=None, *, like=None)\n",
            "    \n",
            "    Return evenly spaced values within a given interval.\n",
            "    \n",
            "    ``arange`` can be called with a varying number of positional arguments:\n",
            "    \n",
            "    * ``arange(stop)``: Values are generated within the half-open interval\n",
            "      ``[0, stop)`` (in other words, the interval including `start` but\n",
            "      excluding `stop`).\n",
            "    * ``arange(start, stop)``: Values are generated within the half-open\n",
            "      interval ``[start, stop)``.\n",
            "    * ``arange(start, stop, step)`` Values are generated within the half-open\n",
            "      interval ``[start, stop)``, with spacing between values given by\n",
            "      ``step``.\n",
            "    \n",
            "    For integer arguments the function is roughly equivalent to the Python\n",
            "    built-in :py:class:`range`, but returns an ndarray rather than a ``range``\n",
            "    instance.\n",
            "    \n",
            "    When using a non-integer step, such as 0.1, it is often better to use\n",
            "    `numpy.linspace`.\n",
            "    \n",
            "    See the Warning sections below for more information.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    start : integer or real, optional\n",
            "        Start of interval.  The interval includes this value.  The default\n",
            "        start value is 0.\n",
            "    stop : integer or real\n",
            "        End of interval.  The interval does not include this value, except\n",
            "        in some cases where `step` is not an integer and floating point\n",
            "        round-off affects the length of `out`.\n",
            "    step : integer or real, optional\n",
            "        Spacing between values.  For any output `out`, this is the distance\n",
            "        between two adjacent values, ``out[i+1] - out[i]``.  The default\n",
            "        step size is 1.  If `step` is specified as a position argument,\n",
            "        `start` must also be given.\n",
            "    dtype : dtype, optional\n",
            "        The type of the output array.  If `dtype` is not given, infer the data\n",
            "        type from the other input arguments.\n",
            "    like : array_like, optional\n",
            "        Reference object to allow the creation of arrays which are not\n",
            "        NumPy arrays. If an array-like passed in as ``like`` supports\n",
            "        the ``__array_function__`` protocol, the result will be defined\n",
            "        by it. In this case, it ensures the creation of an array object\n",
            "        compatible with that passed in via this argument.\n",
            "    \n",
            "        .. versionadded:: 1.20.0\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    arange : ndarray\n",
            "        Array of evenly spaced values.\n",
            "    \n",
            "        For floating point arguments, the length of the result is\n",
            "        ``ceil((stop - start)/step)``.  Because of floating point overflow,\n",
            "        this rule may result in the last element of `out` being greater\n",
            "        than `stop`.\n",
            "    \n",
            "    Warnings\n",
            "    --------\n",
            "    The length of the output might not be numerically stable.\n",
            "    \n",
            "    Another stability issue is due to the internal implementation of\n",
            "    `numpy.arange`.\n",
            "    The actual step value used to populate the array is\n",
            "    ``dtype(start + step) - dtype(start)`` and not `step`. Precision loss\n",
            "    can occur here, due to casting or due to using floating points when\n",
            "    `start` is much larger than `step`. This can lead to unexpected\n",
            "    behaviour. For example::\n",
            "    \n",
            "      >>> np.arange(0, 5, 0.5, dtype=int)\n",
            "      array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "      >>> np.arange(-3, 3, 0.5, dtype=int)\n",
            "      array([-3, -2, -1,  0,  1,  2,  3,  4,  5,  6,  7,  8])\n",
            "    \n",
            "    In such cases, the use of `numpy.linspace` should be preferred.\n",
            "    \n",
            "    The built-in :py:class:`range` generates :std:doc:`Python built-in integers\n",
            "    that have arbitrary size <python:c-api/long>`, while `numpy.arange`\n",
            "    produces `numpy.int32` or `numpy.int64` numbers. This may result in\n",
            "    incorrect results for large integer values::\n",
            "    \n",
            "      >>> power = 40\n",
            "      >>> modulo = 10000\n",
            "      >>> x1 = [(n ** power) % modulo for n in range(8)]\n",
            "      >>> x2 = [(n ** power) % modulo for n in np.arange(8)]\n",
            "      >>> print(x1)\n",
            "      [0, 1, 7776, 8801, 6176, 625, 6576, 4001]  # correct\n",
            "      >>> print(x2)\n",
            "      [0, 1, 7776, 7185, 0, 5969, 4816, 3361]  # incorrect\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    numpy.linspace : Evenly spaced numbers with careful handling of endpoints.\n",
            "    numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.\n",
            "    numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.\n",
            "    :ref:`how-to-partition`\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> np.arange(3)\n",
            "    array([0, 1, 2])\n",
            "    >>> np.arange(3.0)\n",
            "    array([ 0.,  1.,  2.])\n",
            "    >>> np.arange(3,7)\n",
            "    array([3, 4, 5, 6])\n",
            "    >>> np.arange(3,7,2)\n",
            "    array([3, 5])\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "# Create the different demands\n",
        "d32 = bd * np.arange(1, demand_multi + 1)\n",
        "G.printv(d32)\n",
        "help(np.arange)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "id": "8ce635eb2c8b9bb4",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:54.582932Z",
          "start_time": "2024-06-19T09:02:54.241290Z"
        },
        "id": "8ce635eb2c8b9bb4",
        "outputId": "f80d2b07-266d-40c0-b57b-37efc9fa539e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n",
            "/usr/local/lib/python3.10/dist-packages/epyt/epanet.py:14320: UserWarning: WARNING: System has negative pressures.\n",
            "  warnings.warn(errmssg.value.decode())\n"
          ]
        }
      ],
      "source": [
        "# Initialize variable\n",
        "p32min = np.zeros(len(d32))\n",
        "for i, demand_value in enumerate(d32):\n",
        "    G.setNodeBaseDemands(idx, demand_value)\n",
        "    py_idx = idx - 1\n",
        "    p32 = G.getComputedHydraulicTimeSeries().Pressure[:, py_idx]\n",
        "    p32min[i] = np.min(p32)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e6cc50cc6e21b8d",
      "metadata": {
        "id": "1e6cc50cc6e21b8d"
      },
      "source": [
        "#### Plot demand vs pressure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "id": "864433109d62f6dd",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:55.547108Z",
          "start_time": "2024-06-19T09:02:54.585934Z"
        },
        "id": "864433109d62f6dd",
        "outputId": "90ba6ebc-8dec-4215-e2c7-1c01f7e7c9f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x900 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(4, 3))\n",
        "fig.set_figwidth(8)\n",
        "ax.plot(d32, p32min, 'x-')\n",
        "ax.grid(True)\n",
        "ax.set_title(\"Plot demand vs pressure\", fontsize=10)\n",
        "ax.set_xlabel(f\"Base demand (m\\u00B3/h)\", fontsize=8)\n",
        "ax.set_ylabel(\"Pressure (psi)\", fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2758390f373c3735",
      "metadata": {
        "id": "2758390f373c3735"
      },
      "source": [
        "##### How about those negative pressures?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf02f55fab131b34",
      "metadata": {
        "id": "bf02f55fab131b34"
      },
      "source": [
        "### Pressure Driven Simulations\n",
        "\n",
        "##### When the pressure is low, it should approximate zero. This is not the default mode of operation in EPANET. However, it's easy to set it up:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "id": "5e1b88c0e0a82e2c",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:55.554828Z",
          "start_time": "2024-06-19T09:02:55.547108Z"
        },
        "id": "5e1b88c0e0a82e2c",
        "outputId": "5e89ce0a-1216-46cb-b3c4-566b214e543d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "type = 'PDA'\n",
        "pmin = 0\n",
        "preq = 0.1\n",
        "pexp = 0.5\n",
        "G.setDemandModel(type, pmin, preq, pexp)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "960ef6a43c91da15",
      "metadata": {
        "id": "960ef6a43c91da15"
      },
      "source": [
        "Let's try this again"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "id": "b29c25655136502",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:55.564841Z",
          "start_time": "2024-06-19T09:02:55.554828Z"
        },
        "id": "b29c25655136502",
        "outputId": "cc3e646f-73aa-48e2-bddb-ff468fc998df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "# Demand multiplicator\n",
        "demand_multi = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "id": "e60ba1a6f1cfa166",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:55.575620Z",
          "start_time": "2024-06-19T09:02:55.564841Z"
        },
        "id": "e60ba1a6f1cfa166",
        "outputId": "2554aff2-6ada-4905-aad8-88325239b98b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "d32 = [ 100.  200.  300.  400.  500.  600.  700.  800.  900. 1000.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "# Create the different threads\n",
        "d32 = bd * np.arange(1, demand_multi + 1)\n",
        "G.printv(d32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "id": "414914863eb6d14d",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:56.450643Z",
          "start_time": "2024-06-19T09:02:55.575620Z"
        },
        "id": "414914863eb6d14d",
        "outputId": "bc93c325-1daa-47f9-9ef4-ad7b54a1c0d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Initialize variable\n",
        "p32min = np.zeros(len(d32))\n",
        "\n",
        "for i, demand_value in enumerate(d32):\n",
        "    G.setNodeBaseDemands(idx, demand_value)\n",
        "    p32 = G.getComputedHydraulicTimeSeries().Pressure[:, idx-1]\n",
        "    p32min[i] = np.min(p32)\n",
        "\n",
        "# Plot\n",
        "fig, ax = plt.subplots(figsize=(4, 3))\n",
        "fig.set_figwidth(8)\n",
        "ax.plot(d32, p32min, 'x-')\n",
        "ax.grid(True)\n",
        "ax.set_title(\"Plot demand vs pressure\", fontsize=10)\n",
        "ax.set_xlabel(f\"Base demand (m\\u00B3/h)\", fontsize=8)\n",
        "ax.set_ylabel(\"Pressure (psi)\", fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "id": "81d3f1ab914c69b2",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:56.466443Z",
          "start_time": "2024-06-19T09:02:56.455793Z"
        },
        "id": "81d3f1ab914c69b2",
        "outputId": "c8ff780c-67ec-4e47-b611-070eb0e5bdea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Close toolkit for the input file \"Net1\". EPANET Toolkit is unloaded.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "G.unload()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57829e00a5393206",
      "metadata": {
        "id": "57829e00a5393206"
      },
      "source": [
        "### Leakage Simulations\n",
        "#### Load network amd set simulation duration and hydraulic step"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 235,
      "id": "b67a38d1-8f8e-4fe8-b5c1-2c3b134f79ce",
      "metadata": {
        "id": "b67a38d1-8f8e-4fe8-b5c1-2c3b134f79ce",
        "outputId": "bb26c8f0-a25a-48c6-ed16-b84efe85e5cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File downloaded and saved as /tmp/Hanoi_pat.inp\n"
          ]
        }
      ],
      "source": [
        "# Download Hanoi_pat input file\n",
        "url = \"https://raw.githubusercontent.com/WaterFutures/EPyT-and-EPyT-Flow-Tutorial/main/data/Hanoi_pat.inp\"\n",
        "inpname = os.path.join(tmpdirpath, \"Hanoi_pat.inp\")\n",
        "result = download_file(url, inpname)\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 236,
      "id": "2aca29e6c503a249",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:56.548032Z",
          "start_time": "2024-06-19T09:02:56.468447Z"
        },
        "id": "2aca29e6c503a249",
        "outputId": "6b4254f5-91d2-45fc-b23e-d0941b3290d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPANET version 20200 loaded (EPyT version v1.1.9 - Last Update: 24/06/2024).\n",
            "Input File Hanoi_pat.inp loaded successfully.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "d=epanet(inpname)\n",
        "\n",
        "d.setTimeSimulationDuration(7*24*60*60)\n",
        "d.setTimeHydraulicStep(300)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c25e556473227292",
      "metadata": {
        "id": "c25e556473227292"
      },
      "source": [
        "#### Simulate without leak"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "id": "966a81a33ce272a7",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:59.554217Z",
          "start_time": "2024-06-19T09:02:56.561294Z"
        },
        "id": "966a81a33ce272a7",
        "outputId": "3565c7eb-f556-434f-afb2-3fa16de01c5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('Time', array([     0,    300,    600, ..., 604200, 604500, 604800])), ('Pressure', array([[67.27574921, 67.17794037, 67.11522675, ..., 67.36548615,\n",
            "        67.31647491,  0.        ],\n",
            "       [67.27574921, 67.17794037, 67.11522675, ..., 67.36548615,\n",
            "        67.31647491,  0.        ],\n",
            "       [67.27574921, 67.17794037, 67.11522675, ..., 67.36548615,\n",
            "        67.31647491,  0.        ],\n",
            "       ...,\n",
            "       [66.15447998, 66.01503754, 65.93110657, ..., 66.28556824,\n",
            "        66.21385956,  0.        ],\n",
            "       [66.15447998, 66.01503754, 65.93110657, ..., 66.28556824,\n",
            "        66.21385956,  0.        ],\n",
            "       [67.27574921, 67.17794037, 67.11522675, ..., 67.36548615,\n",
            "        67.31647491,  0.        ]])), ('Demand', array([[   70.35491943,    84.78087616,   100.42221069, ...,\n",
            "           92.85502625,    85.87767029, -3397.45532227],\n",
            "       [   70.35491943,    84.78087616,   100.42221069, ...,\n",
            "           92.85502625,    85.87767029, -3397.45532227],\n",
            "       [   70.35491943,    84.78087616,   100.42221069, ...,\n",
            "           92.85502625,    85.87767029, -3397.45532227],\n",
            "       ...,\n",
            "       [   99.61103058,   113.67678833,   118.67366028, ...,\n",
            "          108.62107086,   106.74121857, -4079.28735352],\n",
            "       [   99.61103058,   113.67678833,   118.67366028, ...,\n",
            "          108.62107086,   106.74121857, -4079.28735352],\n",
            "       [   70.35491943,    84.78087616,   100.42221069, ...,\n",
            "           92.85502625,    85.87767029, -3397.45532227]])), ('DemandDeficit', array([[0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       ...,\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.]])), ('Head', array([[ 97.27574921,  97.17794037,  97.11522675, ...,  97.36548615,\n",
            "         97.31647491, 100.        ],\n",
            "       [ 97.27574921,  97.17794037,  97.11522675, ...,  97.36548615,\n",
            "         97.31647491, 100.        ],\n",
            "       [ 97.27574921,  97.17794037,  97.11522675, ...,  97.36548615,\n",
            "         97.31647491, 100.        ],\n",
            "       ...,\n",
            "       [ 96.15447998,  96.01503754,  95.93110657, ...,  96.28556824,\n",
            "         96.21385956, 100.        ],\n",
            "       [ 96.15447998,  96.01503754,  95.93110657, ...,  96.28556824,\n",
            "         96.21385956, 100.        ],\n",
            "       [ 97.27574921,  97.17794037,  97.11522675, ...,  97.36548615,\n",
            "         97.31647491, 100.        ]])), ('TankVolume', array([[0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       ...,\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.]])), ('Flow', array([[3397.45532227,  355.04693604,  270.26605225, ...,  759.16998291,\n",
            "         666.31500244,  580.43731689],\n",
            "       [3397.45532227,  355.04693604,  270.26605225, ...,  759.16998291,\n",
            "         666.31500244,  580.43731689],\n",
            "       [3397.45532227,  355.04693604,  270.26605225, ...,  759.16998291,\n",
            "         666.31500244,  580.43731689],\n",
            "       ...,\n",
            "       [4079.28735352,  429.98794556,  316.31118774, ...,  926.87774658,\n",
            "         818.25671387,  711.51550293],\n",
            "       [4079.28735352,  429.98794556,  316.31118774, ...,  926.87774658,\n",
            "         818.25671387,  711.51550293],\n",
            "       [3397.45532227,  355.04693604,  270.26605225, ...,  759.16998291,\n",
            "         666.31500244,  580.43731689]])), ('Velocity', array([[1.28610921, 0.26617208, 0.18749121, ..., 0.2912384 , 0.26020616,\n",
            "        0.23019816],\n",
            "       [1.28610921, 0.26617208, 0.18749121, ..., 0.2912384 , 0.26020616,\n",
            "        0.23019816],\n",
            "       [1.28610921, 0.26617208, 0.18749121, ..., 0.2912384 , 0.26020616,\n",
            "        0.23019816],\n",
            "       ...,\n",
            "       [1.54421711, 0.32235396, 0.21943399, ..., 0.35557568, 0.31954172,\n",
            "        0.28218302],\n",
            "       [1.54421711, 0.32235396, 0.21943399, ..., 0.35557568, 0.31954172,\n",
            "        0.28218302],\n",
            "       [1.28610921, 0.26617208, 0.18749121, ..., 0.2912384 , 0.26020616,\n",
            "        0.23019816]])), ('HeadLoss', array([[0.157029  , 0.09780844, 0.06271318, ..., 0.05819549, 0.04901432,\n",
            "        0.04072569],\n",
            "       [0.157029  , 0.09780844, 0.06271318, ..., 0.05819549, 0.04901432,\n",
            "        0.04072569],\n",
            "       [0.157029  , 0.09780844, 0.06271318, ..., 0.05819549, 0.04901432,\n",
            "        0.04072569],\n",
            "       ...,\n",
            "       [0.22033589, 0.13944668, 0.08392533, ..., 0.08422228, 0.07170342,\n",
            "        0.05937983],\n",
            "       [0.22033589, 0.13944668, 0.08392533, ..., 0.08422228, 0.07170342,\n",
            "        0.05937983],\n",
            "       [0.157029  , 0.09780844, 0.06271318, ..., 0.05819549, 0.04901432,\n",
            "        0.04072569]])), ('Status', array([[1, 1, 1, ..., 1, 1, 1],\n",
            "       [1, 1, 1, ..., 1, 1, 1],\n",
            "       [1, 1, 1, ..., 1, 1, 1],\n",
            "       ...,\n",
            "       [1, 1, 1, ..., 1, 1, 1],\n",
            "       [1, 1, 1, ..., 1, 1, 1],\n",
            "       [1, 1, 1, ..., 1, 1, 1]])), ('StatusStr', array([['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN'],\n",
            "       ['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN'],\n",
            "       ['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN'],\n",
            "       ...,\n",
            "       ['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN'],\n",
            "       ['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN'],\n",
            "       ['OPEN', 'OPEN', 'OPEN', ..., 'OPEN', 'OPEN', 'OPEN']], dtype='<U4')), ('Setting', array([[120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205],\n",
            "       [120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205],\n",
            "       [120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205],\n",
            "       ...,\n",
            "       [120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205],\n",
            "       [120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205],\n",
            "       [120.60299683, 133.0269928 , 138.96989441, ..., 141.72659302,\n",
            "        141.94360352, 134.46710205]])), ('Energy', array([[1.45266378, 0.09455699, 0.04615108, ..., 0.1202984 , 0.08892708,\n",
            "        0.06436581],\n",
            "       [1.45266378, 0.09455699, 0.04615108, ..., 0.1202984 , 0.08892708,\n",
            "        0.06436581],\n",
            "       [1.45266378, 0.09455699, 0.04615108, ..., 0.1202984 , 0.08892708,\n",
            "        0.06436581],\n",
            "       ...,\n",
            "       [2.44737792, 0.16326608, 0.07228348, ..., 0.21255971, 0.15975739,\n",
            "        0.11504158],\n",
            "       [2.44737792, 0.16326608, 0.07228348, ..., 0.21255971, 0.15975739,\n",
            "        0.11504158],\n",
            "       [1.45266378, 0.09455699, 0.04615108, ..., 0.1202984 , 0.08892708,\n",
            "        0.06436581]])), ('Efficiency', array([[0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       ...,\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.],\n",
            "       [0., 0., 0., ..., 0., 0., 0.]])), ('State', array([], shape=(2017, 0), dtype=int64)), ('StateStr', array([], shape=(2017, 0), dtype=float64))]\n"
          ]
        }
      ],
      "source": [
        "sim_leak_free = d.getComputedHydraulicTimeSeries()\n",
        "print(d.getAllAttributes(sim_leak_free))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "827a0bab4fa6b9fe",
      "metadata": {
        "id": "827a0bab4fa6b9fe"
      },
      "source": [
        "##### Get inflow without leak and create measurements by inserting random noise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "id": "dbbaa436940626a7",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:02:59.566205Z",
          "start_time": "2024-06-19T09:02:59.554217Z"
        },
        "id": "dbbaa436940626a7",
        "outputId": "fa0d63b2-cf54-48ac-d58a-3eb6cb707724",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4247.96810471]\n",
            " [3921.69192389]\n",
            " [4970.95686486]\n",
            " ...\n",
            " [5973.33908622]\n",
            " [5813.73731993]\n",
            " [3941.69721608]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "res_ind = d.getNodeReservoirIndex()\n",
        "res_link = [np.where(d.NodesConnectingLinksIndex[:, 0] == idx)[0][0] for idx in res_ind]\n",
        "Inflow1 = sim_leak_free.Flow[:, res_link] # Inflow\n",
        "eta = np.random.rand(len(Inflow1),1) * np.mean(Inflow1) * 0.0001\n",
        "y1 = Inflow1 * (1 + eta) # Measured inflow\n",
        "print(y1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21e93703712ad2b0",
      "metadata": {
        "id": "21e93703712ad2b0"
      },
      "source": [
        "#### Simulate leakage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "id": "e4fc40d5ef8ebac4",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:02.105318Z",
          "start_time": "2024-06-19T09:02:59.566205Z"
        },
        "id": "e4fc40d5ef8ebac4",
        "outputId": "de4c509e-3436-45ad-fc34-829bbc2c0aaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "leak_node_id = '19'\n",
        "leak_node = d.getNodeIndex(leak_node_id)\n",
        "leak_emit = 20  # Random leak emitter coefficient [2 20]\n",
        "emit = d.getNodeEmitterCoeff()\n",
        "emit = np.zeros_like(emit)\n",
        "emit[leak_node-1] = leak_emit  # Set emitter coefficient (leakage) value\n",
        "d.setNodeEmitterCoeff(emit)\n",
        "sim_leak = d.getComputedHydraulicTimeSeries()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bcd7f1616bfe5fbb",
      "metadata": {
        "id": "bcd7f1616bfe5fbb"
      },
      "source": [
        "##### Get inflow with leak and create measurements by inserting random noise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "id": "aa8a9648377738ab",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:02.782456Z",
          "start_time": "2024-06-19T09:03:02.105318Z"
        },
        "id": "aa8a9648377738ab",
        "outputId": "20c9f5b4-7275-4b43-b2f1-809684f433df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "Inflow2 = sim_leak.Flow[:, res_link]  # inflow\n",
        "Inflow2[:3*288] = Inflow1[:3*288][:]  # change leak start time\n",
        "eta = np.random.rand(len(Inflow2),1) * np.mean(Inflow1) * 0.0001\n",
        "y2 = Inflow2 * (1 + eta)  # measured inflow"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17d042ee-963b-45f2-ae6b-4dfa8422f455",
      "metadata": {
        "id": "17d042ee-963b-45f2-ae6b-4dfa8422f455"
      },
      "source": [
        "##### Plots - Compare inflow in both scenarios"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 241,
      "id": "d6dd0b86-df88-453e-b2f8-c80bd97ebded",
      "metadata": {
        "id": "d6dd0b86-df88-453e-b2f8-c80bd97ebded",
        "outputId": "79a0f754-9172-4792-d68f-fcb614adc9c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(4, 3))\n",
        "fig.set_figwidth(8)\n",
        "plt.plot(y1, color='blue', label='leak free')\n",
        "plt.grid(True)\n",
        "plt.plot(y2, color='red', label='with leak')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Network Inflow')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "573329e6a795c7d",
      "metadata": {
        "id": "573329e6a795c7d"
      },
      "source": [
        "##### Plot leakage location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 242,
      "id": "eebe28cd6c9b2ede",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.432378Z",
          "start_time": "2024-06-19T09:03:02.782456Z"
        },
        "id": "eebe28cd6c9b2ede",
        "outputId": "a6a07775-4abd-4746-f544-b9db5ad58d68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x1800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Custom function to plot the network (alternative to G.plot())\n",
        "def custom_plot(coor):\n",
        "    # Note: Does not used vertices\n",
        "    x_nodes = list(coor['x'].values())\n",
        "    y_nodes = list(coor['y'].values())\n",
        "    # Get links connecting nodes\n",
        "    links = d.getNodesConnectingLinksIndex()\n",
        "    # Plot the network manually\n",
        "    fig, ax = plt.subplots(figsize=(10, 6), dpi=300)\n",
        "    for link in links:\n",
        "        x_coords = [coor['x'][link[0]], coor['x'][link[1]]]\n",
        "        y_coords = [coor['y'][link[0]], coor['y'][link[1]]]\n",
        "        ax.plot(x_coords, y_coords, color='steelblue', linewidth=0.7)\n",
        "    ax.axis('off')\n",
        "    # Plot nodes\n",
        "    ax.scatter(x_nodes, y_nodes, color='blue', s=10, zorder=5)\n",
        "    return ax\n",
        "\n",
        "# Get node coordinates\n",
        "coor = d.getNodeCoordinates()\n",
        "# Custom plot - network plot\n",
        "ax = custom_plot(coor)\n",
        "\n",
        "# Add plot point for the leak node\n",
        "x_leak = coor['x'][leak_node]\n",
        "y_leak = coor['y'][leak_node]\n",
        "\n",
        "# Plot the leak node with a higher zorder\n",
        "ax.plot(x_leak, y_leak, 'o', color='r', markersize=12, zorder=10)\n",
        "ax.text(x_leak + 60, y_leak, 'NodeID: ' + leak_node_id, color='black', fontweight='bold', fontsize=11, zorder=10)\n",
        "\n",
        "# Set the title and show the plot\n",
        "ax.set_title('Leakage location', fontweight='bold')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e38235f3f86a5cf",
      "metadata": {
        "id": "8e38235f3f86a5cf"
      },
      "source": [
        "### Leakage Detection\n",
        "#### Minimum Night Flow Analysis\n",
        "\n",
        "<img src=\"https://github.com/WaterFutures/EPyT-and-EPyT-Flow-Tutorial/blob/main/icons/leakage_detection.png?raw=1\" alt=\"Leakage Detection\" style=\"width: 60%; height: 60%;\">\n",
        "\n",
        "- Basic idea is that each night the uncertainty is low\n",
        "- Demand is low\n",
        "- Pressure is high\n",
        "- Easier to detect leakages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 243,
      "id": "5180b5926f5f2f52",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.441820Z",
          "start_time": "2024-06-19T09:03:03.433406Z"
        },
        "id": "5180b5926f5f2f52",
        "outputId": "2c0c90dd-5866-4904-8c97-6de61e7f5e5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y =  (7, 288)\n",
            "Y = [[3546.52464522 4456.46268049 3906.37107123 ... 4085.07195783\n",
            "  4634.94410497 3843.69816833]\n",
            " [4017.91628475 3607.92337056 4934.36872517 ... 5966.00007346\n",
            "  4497.6917587  4852.95962991]\n",
            " [5036.30279494 4800.00916478 3687.47290724 ... 4687.77014664\n",
            "  4720.94343618 3976.80399203]\n",
            " ...\n",
            " [4525.7711355  5026.59007423 3755.20231295 ... 5402.20532354\n",
            "  6056.59848075 4107.68942567]\n",
            " [4812.32424229 4174.51510118 4786.49152049 ... 5140.29646353\n",
            "  6108.18517914 4595.42594406]\n",
            " [4332.49721522 4315.28136374 5300.92665033 ... 5553.99326958\n",
            "  4752.5474435  3994.02952504]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "# Compute the minimum flow for each day\n",
        "Y = np.reshape(y2[1:], (7, 288))\n",
        "print('Y = ', Y.shape)\n",
        "d.printv(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "id": "64e520af5d00c90d",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.450518Z",
          "start_time": "2024-06-19T09:03:03.441820Z"
        },
        "id": "64e520af5d00c90d",
        "outputId": "a56eb72b-1098-40e8-f1fe-98f46e1d6540",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MF = [2375.09882112 2317.74746457 2368.2925976  2436.16929326 2542.86918916\n",
            " 2451.69904511 2395.55936857]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "MF = np.min(Y, axis=1)\n",
        "d.printv(MF)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "id": "5fa38b1c2a28e5d",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.978181Z",
          "start_time": "2024-06-19T09:03:03.450518Z"
        },
        "id": "5fa38b1c2a28e5d",
        "outputId": "444d8ef7-5d17-4a07-af9a-5cbcf7052f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of change points: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x900 with 1 Axes>"
            ],
            "image/png": 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27do5LPg7YzKZNHr06OvuuWvXLi1dutTpuGuf3k9NTXU7v7+lpDhuG+dsR4Cbvde193F1LwAAAAAAAOSNefMOad++hfL2TncaEx1dVWPHdpfVmvMHUQDkj0qVAvXmm08pPr6uy7jg4EgtWTJDX311zDOJAf8vNjZWjz/+uMPaQsmSJbV+/Xr17NlTBoPB7Wv961//0u+//65+/frlRaoAgFxQaI4V+PLLLzVo0CCHvt69e2vChAl5fu8XX3xR33zzjX755ZfMvnnz5qlz585ZxgcGBjq0r3263x3XPr1/7TVz615Z7RLg7F45MXDgQHXt2jVHY44ePaqOHTve9L0BAAAAAAAKkxkz9uv06aXy8rI5jYmJqaG33uosPz+TBzMDkBsCArw0cWJ7TZ5cSomJP8pozHpXWl/fZO3b94VOnHhQgwc3ltHo/qIscKNGjx7tcLyy0WjUsmXLVLdu3Ru6ntls1owZM1w+YAkAyD+Fojjg+++/V69evRy28u/UqZNmzJiRo6q1m/HKK684FAf8/PPPSk9Pl5fX9X+E1y6uJyQk5Ph+145xtzggp/e6Nt7LyytXdg4oWbKkSpYsedPXAQAAAAAAKMo++mivLl/+ViaT8yMsY2Pv1IQJHWU2F6pNQAH8g9Fo0Guv3a1Fi4prz54l8vW9fkfXv+LsSkr6Qa+9dkljx7alIAh5Kjo6WtOnT3foGzRokO65556bvnanTp1ueOyhQ4e0bds2nT9/XiaTSSVLllTjxo1Vo0aNm8opPj5e+/fv159//qnIyEglJSUpJCRExYsXV/369XXHHXfc1PWd2bt3r3bu3KnLly/Lx8dHpUuXVtOmTRUWFnbT1z5y5Ih27Nih8+fPKz09XWXKlFHDhg1Vq1atm0/8GhkZGdq5c6eOHj2qy5cvKyUlRSVKlFClSpXUrFmzLI+x9rTIyEht3LhRx48fV3JyskqXLq077rhDTZs2dWtH8exER0dr8+bNOn/+vK5cuaLAwECVLFlS9erVy7N/P+6y2Wzavn279u7dq8jISAUEBKhMmTJq0aKFSpcuna+5nTp1Srt379bZs2cVGxsrg8GQmV/lypVVq1YtdjX3oAJfHLBu3Tp17dpV6en/21KtTZs2WrBggUwmz/1g1Lp1axkMhswChbi4OF24cEEVKlx/XtS1i+Lnzp3L8f2uHeNsof3a/rNnz97UfUqUKJGj8QAAAAAAALgxU6fuUnT093L1WXV8fF1NmNBO3t4UBgBFweOP364qVfprwYIFCg6OdBoXGLhbw4ZFatCgxxQWFuDBDHEr+fTTTxUfH5/ZNpvNGjZsWL7l8+OPP2r48OHauXNnlq/XqFFDEydOVLt27dy+5uHDh7VgwQL98MMP2rlzp8Na07VKly6tAQMG6Pnnn5fVanXr+uvXr1erVq0y26NGjdLo0aMlSQsWLNCYMWN06NChLMc2adJEU6ZM0b333uv2+/nb6tWrNWzYMO3evTvL12vXrq3x48erQ4cOkqTw8HCHB2D/+TBudk6ePKlx48Zp2bJlunr1apYx/v7+6tSpk8aOHatKlSrl4J3kTO/evTVnzpzM9okTJxQWFqaTJ09qyJAhWrp0qTIyMq4bV7ZsWb388ssaNGjQDa0tbtiwQWPGjNGGDRuc/hu6/fbbNXDgQD333HMym907Aionfy+zZ89Wnz59MtuzZs1S7969ZbPZNG3aNE2YMCHLNUKDwaAHHnhAU6ZMUe3atZ1e/9o/27+dOnXK5YPavXr10uzZs6/rt9lsmjFjhj766CP9/vvvTsdLf809DRo0UOfOnTVw4ED5+fm5jMfNKdDFAdu2bVP79u0dtspv2rSpvvnmG7e/sHJLQECArFarw8QXERGRZXFA5cqV5eXllTlBJCUlKSIiIkcL76dPn3ZoV69ePcu4atWquRyXW/cBAOCfzsae1b2fZ/0fl019N6l8cHkPZwSgKGGOAZDXmGdQEEyatFVJSavlalPMxMSGmjDhEZlMbC1e2DDPwJWGDYupQoWnNWHCElksx5zGWa2n9dFH09WuXTe1aJG/T32i4EnNSNWfV/7M8rXqxavLbMp+DeXarf//9a9/qXjx4rmSX04NGTJEkydPdhlz8OBBtW/fXmPHjtWIESOyveb333+fo0KCixcvatSoUfrss8/0zTffqFGjRm6P/afU1FT17dtXX3zxhcu4bdu2KTw8XDNmzFDv3r3durbdbtfzzz+vjz76yGXcvn371LFjRw0aNEjvvfeeu6lfZ/z48Ro3bpxSU1NdxiUmJmr+/PlavHixPv74Y/Xr1++G75lT69atU4cOHRQXF+c05vz58xo8eLC++uorrVy5UqGhoW5dOzU1VU8//bTmzZuXbezRo0f18ssva9q0aVq+fLlH1ttiY2PVqVMn/fTTT05j7Ha7Vq9erY0bN2rp0qV68MEH8zyvqKgotWvXTps3b3YrPjU1VVu2bNGWLVvUoUMH3X777Xmc4a2twBYH/P7773r44Ycdqtbq1aunlStXKiAgfyolvb29HdppaWlO46pUqeJQDXbgwAG1bNnSrfukpKQ4nPEjOV+0v7b/wIEDbt3jbwcPHnTrPgAA/FO6LV2nYk45fQ0AbgZzDIC8xjyD/DZ+/CZlZDj/EFeSUlPv0dtvt+HM8UKKeQbZKVXKVxMn9tDo0Wvk47PVaVxgYIxWr/5cR492VN++NT2YIQo6u92u1IysF2zdeSo8ISFBv/32m0Pf30+Ze9rQoUMdCgOCgoJUsWJF+fn56dSpU4qIiHCIHzlypGrVqpXt0QX/fPD0b35+fqpQoYKCgoJkMBgUGRmpU6dOyWazZcacO3dO4eHh2rlz5w0dZdCrVy8tXLgws221WlWhQgV5eXnp+PHjio6OznwtIyNDTz/9tGrVquVWMcJ//vMfffzxx9f1Fy9eXBUrVlRKSopOnjyZeaT01KlTb2jH6IyMDPXr1y/LJ8mLFSumcuXKyWw268KFCw47VP+9mB4bG6uXXnopx/fNqQMHDujxxx/PXEs0GAwKCwtTsWLFFBERoVOnHL8Xb9u2TQ8++KB+/vlnBQUFubx2SkqKHn30Ua1du/a618qUKaNy5copNjZWJ06ccFgvPHLkiO69916tWbNG9erVy4V3mbW0tDQ9+uij2rhxY2ZfyZIlVb58eaWnp+vYsWMOR4snJiaqS5cu2rdvn2677bY8y8tut6tDhw7XFQYYDAaVKVNGpUuXlre3t+Li4nT+/HmHrwd4RoHcj+zQoUNq06aNoqKiMvtq1Kih1atXKyQkJF9ySk9PV2Sk4zZPribUunXrOrR//fVXt++1a9cupaT878ypMmXKOD1W4Nr77Nixw+W2ONe69ovz2usBAAAAAAAgd9hsdo0evS7bwgCbrYXGjaMwACjqzGaj3nrrQYWGtld6uvNtrs3mNJ0585XGjFmvjAz3twIHXNm6det1awkNGzb0eB4bNmzQxIkTJf21zf6PP/6oq1evat++fdqxY4cuXbqkdevWXfdg4wsvvODWWojJZFLbtm316aef6siRI4qPj9ehQ4e0c+dO7dixI3Oxfv78+Q5nxicmJqpHjx452n5fkubNm5dZGPDQQw9py5YtioyM1N69e7Vr1y5duXJF33zzjcqWLZs5JiMjQ//5z3+yvfY333xzXWFA8+bN9euvv+ry5cvatWuX9u3bp8jISC1atChz5+vRo0fr2DHnu5RkZezYsQ6FAd7e3nrxxRe1f/9+XblyRXv37tWOHTt09uxZHT16VP/+978dtp4fMmRIjtbFbtTAgQMVHx8vg8Gg559/XidPntTx48e1Y8cOnTx5UkePHnXYil+Sdu7cqSFDhmR77ddff/26woCOHTtq7969On/+vHbs2KFDhw7p4sWLmjRpksNW+JGRkeratavDA9C5bcKECZmFAU888YR+//13Xbp0Sbt27dLevXsVGRmpzz//XMHBwZlj4uPjnb73IUOGaM2aNVqzZo1KlSqV2R9aIlQfLvjwul8rflihNWvWXHe9JUuWOBQsBAQEaPLkyZmFJLt27dLWrVu1f/9+RUVF6fTp05ozZ446deokL68C+0x7kVLg/pRPnTql+++/X5cvX87sq1SpktasWXND1U255dpvlF5eXipd2vlWTo8++qgWLVqU2V6zZo3bZ/WsWbPGoe1q25vq1aurSpUqmRN7QkKCfv31V7Vo0SLb+yQkJGjLli2ZbYPBoEcffdStHAEAAAAAAOA+m82uESPWymx2/UG5ydRao0Y191BWAAqC55+vp5Uri+uXXxbJ3z/BReQvevXVCI0Y0UFWq2eO3T0ZfTLPrl0+uLy8jNkvUZyNPZtnO26UDiwtXy/fbOMuxl9Ucvr1T6HnVJgl7KavkVv+/NPxSILAwMB82cr7xIkTkv4673zGjBnXnQdvMBgUHh6uDRs2qG7dujp//rykv57uX7FihcvdDho0aKBjx45l+5R0UFCQnnjiCXXu3FmdO3fWypUrJUl79uzRDz/8oIcfftjt9/P3rtAjRozQ2LFjr3vdZDKpY8eOqlmzpurVq6fExERJ0vbt27V3717dddddWV43JSVFzz33nENfz549NWfOHBmNjs8B+/j46LHHHtN9992nFi1a6MCBA1meRe/Mr7/+qvHjx2e2ixcvrlWrVjktHqlSpYo++eQTtWrVSj169JDNZlN6eroGDBigvXv3un3fG3Hq1CkZDAbNnTtXPXv2zDK3zz//XHfddZcGDRqU2f/pp5+qd+/eatKkSZbX3bFjx3XHMYwcOVJjxoy5LjY0NFSvvvqqWrdurdatWys2NlaSdOzYMb3xxhuaOnXqjb9BF44fPy6DwaDPPvtMTz/99HWv+/j4qE+fPrr99tsVHh6euTvGN998k+VR6DVr1lTNmn/tUOPr6+twnSYtrv9zurPknfLx8rmu/6uvvnJof/fdd2rVqpXT91GhQgU99dRTeuqpp3TmzBlZrVYX7xq5oUAVB1y4cEH33XefwyRVrlw5/fTTTypXrlw+ZibNnDnToX3PPffI39/fafwjjzwiLy+vzIKC9evX6/jx46pcubLL+9jtds2ePduhL7utfNq3b+8wSc2cOdOt4oBFixY5VC01bNjQoVoNAAAAAAAANy8jw67hw1fJz2+Hyzhf3wf12mt3eygrAAXJI49UUFhYf02fvlAWy0WncSEhBzR27FX1799NNWvm/S67ld6vlGfXPvHiCbcWy+/9/F6nx3TcrHW91ik8LDzbuG5LuumXU7/c9P3sowrOzg9Xr151aJcoUcLhyW9PatiwoT777LPrCgP+qUSJEhoxYoQGDBiQ2bdq1SqX6yeVKuXs36+vr6/mzZunypUrKyYmRpI0a9asHBUHSH+t6WRVGPBPd9xxh55//vnMXROkv96Ps+KAJUuW6MKFC5ntatWqaebMmdcVBvxTsWLF9PXXX6tOnTpOj8nOytixYzMXko1Go7799lu3dpV4/PHHtWvXrswjIn7//XetXbtW999/v9v3vhHPPfdcloUB//Tiiy9q69atmbs62O12vf/++/ryyy+zjH/vvfccdo149NFHsywM+KcGDRros88+U7du3TL7ZsyYoTFjxuTZrugvvPBCloUB/9S8eXN17do184HmtLQ0/fTTTw555qbDhw9n/r569eouCwOu9fduF8hbBeZYgatXr6pNmzYOW5uUKFFCa9asyfEEntvWr1+vefPmOfR17NjR5ZjQ0FCHGLvdrtGjR2d7r88//1wnT57MbN92223ZTpx9+/Z1+Ka9cOFCHTx40OWY5ORkTZgwwaGvX79+2eYHAAAAAAAA96Wl2TR06HfZFgYEB7elMAC4xdWsGaKRI/soJqamyziL5aLmzJmuFStOeygzFEXXFgdYLJb8SUR/LUZ7e3tnG/fYY485tH/77bdczyU0NNShGOBGtsZ/66233Ip7/PHHHdqu3s/cuXMd2mPGjJHZnP0OItWrV1fv3r3dykeSDh48qNWrVzvk2LRpU7fHv/baaw5bw3/99dduj70Rvr6+2S7a/23ChAkOxRRLly7N8rz76Ohoh7wNBoPeeecdt+7x+OOP6+67//fzXEJCgtMChJvl5+enESNGuJ3XP+XF187fkpKSMn/vztc1PK9AFAfExcXpoYce0v79+zP7LBaLfvzxR9WoUSPX7rNmzRrNmjXLrXNo/vbzzz+rU6dOysjIyOwrU6aMnn322WzHjhkzxmGimTdvnhYsWOA0/sCBAxo8eLBD34gRI7Kd4GvXru3wTTE1NVW9evXK3LrkWna7XYMGDdKRI0cy+ypXrqy+ffu6vA8AAAAAAADcl5pq09ChyxQYuNtpjM1mUPHiHfTSS54/5xlAwWO1mjVlShdJ4S7j/P0TtG3bHP33v87nF8CVuLg4h3ZAQEC+5BESEqIHH3zQrdjQ0FBVrFgxs33mzJk8yemfD6yeO3dOERERbo+98847M7dmz07t2rUdFtKdvR+bzeZwRHRAQEC2O07/U3ZP1f/TqlWrHNpPPvmk22Olv3YraNCgQWb7n2fP54W2bdsqNDTUrdjbbrtNLVu2zGynpKRo69at18Vt2bJFqampme17771Xd9xxh9s5XbvWtmHDBrfH5sT999+vYsWKuRVbt25dh3Zefe1Ictih/MCBA9q9m+9TBU2BKA5o3769duxwrJ5++eWXdeXKFa1duzZHv6Kiopze59y5c+rbt6/CwsL00ksvad26dZlbw/xTRkaGtm3bpl69eqlNmzYO1zQajfroo49cHinwt5o1a163nUfPnj01cuRIh2umpaVp9uzZuvfeex2qlOrUqaNevXplex9JGj9+vENOO3bsUIsWLbR+/XqHuMOHD6tLly769NNPHfonTJhABQ8AAAAAAEAuSUrK0NChSxQc/IfTmIwMoypU6KznnqvrucQAFHhGo0GjRrVUhQpdlZrq/DNbk8mmq1eX6/XXVys11ebBDFEUBAUFObQTEhLyJY/69eu73Br/WiVLlsz8fVbrO85ER0dr5syZ6tOnj+rXr68yZcooICBABoPhul9vv/22w9grV664fR93tt//m7e3t8OODc7ez6FDhxyKOerXr+9wJnx2Gjdu7FCE4Mq1i/k5eT9/+2cBx59//umwPX9uCw8Pv6n47du3Xxezbds2h3br1q1zdI/77rvPoZ1VAUJuyMnfzT+/bqScfe3kVJs2bTJ/n5GRoYceekgzZsxQYmJint0TOePebJDHrl3AlqSRI0fe0LXWrVuX7WRw7tw5TZ06VVOnTpUklStXTqGhoQoICFBsbKxOnz6t+Pj468YZDAZNnTpV//rXv9zO57333tNvv/2mnTt3SvqrwmvcuHGaOHGiKlWqJB8fHx0/fvy6+xUvXlxfffWV2xP27bffrpkzZ6pHjx6ZE+3evXvVqlUrlShRQhUrVtTly5d19uzZ6ybi559/Xl27dnX7PQEAAAAAAMC5+Ph0jRixWBbLEacx6ekmVa3aRU89Vd2DmQEoTPr2rakNG0L13XcLFRjofCHHx2erXnstQkOHdlGpUu4vGOLWdu3T1nm5WOjKtYuW2fnnDgf/3L7cmYSEBI0ZM0YffPCBUlJScpyfpCy3nnfmRt7P38UHzt7PuXPnHNo53XHb19dXYWFhOnr0aLax1x5ZndP3c62MjAzFxsYqJCTkpq7jTO3atW8q/sSJE9fFnDp1yqFdp06dHN2jcuXKCgoKyizoOHPmjOx2u8Px4LkhJ3831+4M4s7Xzo169tln9cEHH+jSpUuSpMuXL6t///568cUXdf/996tVq1Zq1qyZ6taty0PL+aRAFAfkt3Pnzl03uV6rTJkymjNnjkPFizv8/f21evVqde3aVT///HNmf2pqqg4dOpTlmLCwMC1fvjxH25RIUrdu3WS329WvXz+HL+yIiAin294MHjxYkyZNytF9AAAAAAAAkLWoqFSNGbNIVutxpzFpaV6qXftxdet2uwczA1AYtWhRWhUr9tfUqYtltZ52GmexHNOkSTPUrVs31atn8VyCKLSuLQ7Iydb5uSknT8Dn1JUrV9S6dWv98YfzXXzckZOigpt5P86esL+2OOFGFtr/uUOBK5GRkTm+dnZiYmLyrDjA3W31ncVnVfhx7Q7lxYsXv6G8/i4OyMjIUFxcnIKDg3N8HVfy4t9abggNDdX333+v9u3b68KFC5n9iYmJWr58uZYvXy5JCgwMVPPmzdWpUyd16dLF7X+juHm3VHFA69atNWbMGK1bt067du267kydaxmNRtWtW1fPPPOMnnzySbeOEshKaGio1qxZo5kzZ2rSpElOq7NCQ0P17LPPatiwYQoMDLyhe3Xv3l1NmjTRiBEj9NVXXyktLS3LuBYtWmjs2LEO56sAAAAAAADgxkVEpOjNN790uYCXmuqthg2761//quQ0BgD+KSwsQG+//ZRGjlyhwEDnZzcHB0dq6dIZOnmys3L4MK1TJ168/qna3FI+uLxbcZv6blK6LT1PcigdWNqtuIVdFio5PTlPcsgv1as77lwTFxeno0eP6vbbi07hWteuXa8rDKhQoYJatWqlmjVrqnz58goMDJSfn5/D0QZz587VvHnzPJ2uU9cWJ5jN5hxfw8fHx624nOyS4C6bLe+OPcnput21T9BntYv4tX3XjrmR++RFcUBB1rBhQ+3bt08TJkzQzJkzdfXq1eti4uPjtWrVKq1atUovv/yyXn75ZQ0fPpzdBDygQBQH5GWFyj9VrFhRI0eO1MiRI2W323Xs2DEdPXpUZ86cUXR0tJKTkxUQECCr1aoKFSqocePGufbFajQa1b9/f/Xv319//PGHfvvtN124cEEZGRkqVqyYateurSZNmuTKP/rKlSvriy++0LRp07Rp0yYdOXJEcXFx8vX1VcWKFdWsWTOVK1cuF94VAAAAAAAAJOnChSRNmvSFrFbnu1OmpPjo3nuf0COPVPBgZgCKAj8/kyZObKd33iml+PjVMhqz/kzd1zdFhw59peLF26hUqUDd7C7WYZawm7tALnC3iCAvuVtEUJjcfffd8vLyUnr6/wovdu7cWWSKA5YvX+5wpHVQUJCmTZum7t27OxQCZOWnn37K4+xy5tqn7rNa0M5ObGysW3H+/v4OsatWrXL7+GtnSpfOu6+fnJ5jn5CQ4NDO6kHda/uuHXMj9wkKCsrxNQq70NBQTZo0SePHj9f69eu1bt06/fLLL9q5c+d1DzbHxcVpzJgxWrt2rX788ccbflgb7ikQxQH5wWAw6Pbbb8+Xb3R33nmn7rzzzjy/T3BwsB555JE8vw8AAAAAAMCt7PTpBL333nxZLBedxiQl+alNm566776yHswMQFFiNBr06qtNtGRJCe3a9ZV8fbN+kt1olGy2WJ07l65y5Sw3XSCAoikgIED169fX9u3bM/uWL1+ubt265WNWuWfhwoUO7U8//VTdu3d3a2xWTznnp2uPgDh//nyOr/HP7d1dKV68uENxQP369XN0tr2nXblyJUfx1x6bkNVW9lar1eWYnN7HZDLdksUBfzObzXrggQf0wAMPSJKSkpK0detWrVy5Ul9++aXDv+fNmzdr8ODB+vjjj/Mr3VuC6/IoAAAAAAAAAE4dOxanqVPnuCwMSEz0V9u2vSgMAJArunSprC5dnlZMjOtzsI3GRKWlpctm88zOvSh8/vWvfzm0ly5dmidnzueHrVu3Zv6+WLFieuyxx9weu3///rxI6YbVqFFDhn9U+eze7fx4kaycPXvW7UX0SpUcjz1ydkx2QbFv374cxV97zMS171eSbrvtNof23r17c3SP48ePOxxrXrFiRYe/v1udn5+fWrVqpcmTJ+vkyZMaPny4w+szZsxwe6cL3BiKAwAAAAAAAIAbcPBgjD7+eLZCQiKcxiQkBKlz5z5q3ryUBzMDUNQ1aFBMQ4f2U3S0651xDQa70tPTFR+f5jIOt6Z///vfDluop6SkaOLEifmYUe65dOlS5u9vv/12mUwmt8bFxsZq165deZXWDbF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wpSouLlZ+fr4h17FjRztVA6A12rBhg9UP2g4fPqzHH3/cEnfs2FHvv/++YUzPnj2bpT4AqLnrEe+tANRl9OjRcnFxUXV1tSQpLS1N+fn5tXZSq893331niHv16tXsNaL50BwAOLCoqCgFBQUpNzdXkpSamqodO3bUOr+uLitXrjTE48aNs0mNAAAAjcnOztaIESOUmZlpyYWGhmrbtm0KDQ21Y2UArmSbNm2S2Wy2xMHBwerUqZMdKwLQ2gwdOtTqOW3aGD+O9fDwUExMTHOVBAAG33zzjSGuecwAAEhShw4dNGTIEO3evduSW7t2raZOndro3KqqKq1bt86QGzZsWHOXiGbEsQKAA3NxcVFcXJwhFx8fb/gAqy7btm0z3OR9fX01YcIEW5QIAADQoPz8fMXGxurkyZOWXHBwsLZs2aLw8HA7VgbgSlZWVqYFCxYYcmPGjJGLCx+jAAAAx1BYWKhPP/3UkBsxYoSdqgHQ2k2bNs0QL1myROfPn2903ltvvWXYpaRdu3a64447mr0+NB/e1QIObu7cuYbjAHbu3Gk4aqCmrKysWt1es2bNUlBQkM1qBAAAqEtxcbHuvPNOHT161JLz9/fX119/rT59+tixMgDOYs6cOdq3b59Vc/Lz8zV27Fj9+OOPlpyrq6tmz57d3OUBAADYzOOPP67CwkJL7O7urpEjR9qvIACt2qRJk3TddddZ4h9//FHTpk2zHDVQl6SkJM2ZM8eQmzFjhvz8/GxWJy4fxwoADi4oKEjz5s3TvHnzLLmnnnpKGRkZmj9/vkJCQiRJ1dXV+uyzzzRr1ixlZGRYxoaEhOixxx5r8boBOJY9e/aorKysVv7w4cOGuLy8XFu3bq3zGiEhIerbt69N6gPgmMaOHVvrod2jjz6q3Nzceu8l9YmMjFRAQEBzlgfACXz99ddasmSJBg8erIkTJ+q2225Tv3795ObmZhhnNpuVnJysTz75REuXLrUc3fYfs2fPNnxQBgAA0FIWLVqk2NhYRUZGNml8VVWV5s6dW+tY2enTp3NEEoB6ubi46JVXXlFsbKxld+p33nlHWVlZWrRokeEeVFRUpJUrV2rBggUqKSmx5K+55hrDsyq0TiZzY/uPA2j1qqurNW7cOG3cuNGQd3V1VdeuXeXn56e0tDRDp6gkeXp6asuWLRoyZEgLVgvAEXXr1k2nTp26rGtMnjxZb7/9dvMUBMApmEymZrtWQkICZ9oBqCUiIqJWM6O7u7tCQ0Pl7+8vd3d3FRcX6/Tp0youLq7zGpMnT9aqVas4UgBAs9ixY4eGDx9uibt27ar09HT7FQSg1Rs2bJh27typqKgoTZgwQSNGjFDv3r3Vpo3xu59FRUXavHmzXnrpJR06dMjwWo8ePZSUlKTAwMAWrByAI1q8eLGefPLJWvmrrrpKYWFhOnfunE6ePKmKigrD64GBgUpISKCp2gGwcwDgBFxcXPTJJ5/ogQce0EcffWTJX7hwQampqXXOCQwM1Jo1a2gMAAAAAHBFqaioUFpaWqPj2rVrp0WLFmn69OnN2swEAABwKRITE5WYmChJatu2rcLCwuTn5ydXV1fl5eUpPT29zu2/r7rqKn3xxRc0BgBokrlz58rLy0uPPfaYKisrLfkzZ87ozJkzdc7p1auXPv/8c1199dUtVSYuA23vgJPw8PDQ6tWrtWbNGkVERNQ7ztvbWzNmzNCxY8f4dh0AAAAAp7Z69WotXrxYMTExateuXaPjTSaT+vfvryVLliglJUUPPfQQjQEAAKDVOX/+vE6ePKkDBw5o3759Sk1NrbMxYNSoUTp8+DAP7ABY5a9//auOHDmiiRMn1jqS7WLh4eF67bXXdOTIEe4zDoRjBQAnlZKSoqSkJGVlZamiokL+/v7q06ePhgwZIg8PD3uXBwAAAAAtqrq6Wj/99JNSUlKUkZGhX3/9VZWVlfL19ZWfn5+6deumgQMHNqmJAAAAoKVs2bJFa9eu1e7du3XixAlduHChwfE+Pj4aOXKk/vKXvyg6OrqFqgTgrH799VclJibqp59+UlFRkXx8fNSxY0cNHDhQvXr1snd5uAQ0BwAAAAAAAAAAALRypaWlOnbsmNLT05Wdna2SkhJVV1fL399fAQEB6tu3r6677jq5urrau1QAQCtFcwAAAAAAAAAAAAAAAE7Oxd4FAAAAAAAAAAAAAAAA26I5AAAAAAAAAAAAAAAAJ0dzAAAAAAAAAAAAAAAATo7mAAAAAAAAAAAAAAAAnBzNAQAAAAAAAAAAAAAAODmaAwAAAAAAAAAAAAAAcHI0BwAAAAAAAAAAAAAA4ORoDgAAAAAAAAAAAAAAwMnRHAAAAAAAAAAAAAAAgJOjOQAAAAAAAAAAAAAAACdHcwAAAAAAAAAAAAAAAE6O5gAAAAAAAAAAAAAAAJwczQEAAAAAAAAAAAAAADg5mgMAAAAAAAAAAAAAAHByNAcAAAAAAAAAAAAAAODkaA4AAAAAAAAAAAAAAMDJ0RwAAAAAAAAAAAAAAICTozkAAAAAAAAAAAAAAAAnR3MAAAAAAAAAAAAAAABOjuYAAAAAAAAAAAAAAACcHM0BAAAAAAAAAAAAAAA4OZoDAAAAAAAAAAAAAABwcjQHAAAAAAAAAAAAAADg5GgOAAAAAAAAAAAAAADAydEcAAAAAAAAAAAAAACAk6M5AAAAAAAAAAAAAAAAJ0dzAAAAAAAAAAAAAAAATo7mAAAAAAAAAAAAAAAAnBzNAQAAAAAAAAAAAAAAODmaAwAAAAAAAAAAAAAAcHI0BwAAAAAAAAAAAAAA4ORoDgAAAAAAAAAAAAAAwMnRHAAAAAAAAAAAAAAAgJOjOQAAAAAAAAAAAAAAACdHcwAAAAAAAAAAAAAAAE6O5gAAAAAAAAAAAAAAAJzc/wNyxNSW6Bsa6wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate change indices based on threshold\n",
        "threshold = 3\n",
        "changeIndices = np.abs(np.diff(MF)) > threshold  # Find where the absolute difference between consecutive elements is greater than the threshold\n",
        "changeIndices = np.insert(changeIndices, 0, False)  # Add False at the beginning to align with MF length\n",
        "\n",
        "# Create a new figure and axis for plotting\n",
        "fig, ax = plt.subplots(figsize=(8, 3))\n",
        "\n",
        "# Plot the original data\n",
        "ax.plot(MF, color='blue', label='Input data')\n",
        "\n",
        "# Plot the segment mean (in this case, it's the same as the original data)\n",
        "ax.plot(MF, color='grey', label='Segment mean')\n",
        "\n",
        "# Find the indices where changes occur\n",
        "indices = np.where(changeIndices)[0]\n",
        "x = np.repeat(indices, 3)  # Repeat each index 3 times to align with y3 for plotting vertical lines\n",
        "\n",
        "# Get the current y-axis limits\n",
        "ymin, ymax = ax.get_ylim()\n",
        "ylim_nan_column = np.array([ymin, ymax, np.nan])  # Array to create vertical lines that span the y-axis\n",
        "\n",
        "# Count the number of change points\n",
        "nnz_changeIndices = np.count_nonzero(changeIndices)\n",
        "print(\"Number of change points:\", nnz_changeIndices)\n",
        "\n",
        "# Create y-coordinates for change points using tile and flatten\n",
        "y3 = np.tile(ylim_nan_column, (nnz_changeIndices, 1)).flatten()\n",
        "\n",
        "# Plot the change points as green vertical dashed lines\n",
        "ax.plot(x, y3, color='green', linestyle='--', label='Change points')\n",
        "\n",
        "# Set the title to show the number of change points\n",
        "ax.set_title(f'Number of change points: {np.count_nonzero(changeIndices)}')\n",
        "\n",
        "# Add a legend in the upper right corner\n",
        "ax.legend(loc='upper right')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0ae277e4d6a4c59",
      "metadata": {
        "id": "d0ae277e4d6a4c59"
      },
      "source": [
        "#### Detecting changes\n",
        "- Change detection algorithms\n",
        "- Limit checking, change in mean\n",
        "- Cumulative Sum (CUSUM)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "580ff06f5c6921d5",
      "metadata": {
        "id": "580ff06f5c6921d5"
      },
      "source": [
        "##### Limit checking, the most simple detection algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "id": "a66b9e1a8ce486d5",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.987272Z",
          "start_time": "2024-06-19T09:03:03.978181Z"
        },
        "id": "a66b9e1a8ce486d5",
        "outputId": "55fe67e5-44c4-4c4e-d59e-978cf02cba0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "threshold = 30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "threshold = 30\n",
        "d.printv(threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "id": "3f9614e2140a5456",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:03.996670Z",
          "start_time": "2024-06-19T09:03:03.987272Z"
        },
        "id": "3f9614e2140a5456",
        "outputId": "6937296e-b8ef-43e9-a394-d011409db80e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alert = [False False False  True  True  True  True]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "alert = MF-MF[0] > threshold\n",
        "if np.any(alert):\n",
        "    # Find the first occurrence of True\n",
        "    first_true_index = np.argmax(alert)\n",
        "\n",
        "    # Set all elements from the first True to the end to True\n",
        "    alert[first_true_index:] = True\n",
        "d.printv(alert)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "id": "43f0324cbc9d8649",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-06-19T09:03:04.242995Z",
          "start_time": "2024-06-19T09:03:03.996670Z"
        },
        "id": "43f0324cbc9d8649",
        "outputId": "0fac55d3-ba34-4745-921a-8c59ddf20de6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "plt.plot(alert, 'r-')\n",
        "fig.set_figwidth(8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a1b7436d500797d",
      "metadata": {
        "id": "3a1b7436d500797d"
      },
      "source": [
        "##### A different approach would be to take a window of minimum (night) flows, and compare them with the new minimum flow. If it exceeds a precalculated distance, the issue an alert."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (Spyder)",
      "language": "python3",
      "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": "3.10.14"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}