data_study_for_heat.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMCgdKAkHpG4Zyc6qgBP/7X",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yajuna/tensorflow_pde/blob/master/data_study_for_heat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# This notebook plots the data we use to study modeling of the heat equation.\n",
        "\n",
        "Data collected are from Aug 21 to Aug 27, in 2022. We will plot the data of the full 7 days, as well as each day, and compare the changes."
      ],
      "metadata": {
        "id": "F0GngXw-AlOu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3F4c3Vz5AYVQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60d8e3c6-5e4e-4407-b5e9-ea50543279da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3 version is 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]\n",
            "Matplotlib version is 3.7.1\n",
            "Tensorflow version is 2.15.0\n",
            "Numpy version is 1.23.5\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas\n",
        "\n",
        "from time import time\n",
        "\n",
        "import sys\n",
        "print(\"Python 3 version is\", sys.version)\n",
        "import matplotlib\n",
        "print(\"Matplotlib version is\", matplotlib.__version__)\n",
        "print(\"Tensorflow version is\", tf.__version__)\n",
        "print(\"Numpy version is\", np.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DTYPE = 'float64'\n",
        "tf.keras.backend.set_floatx(DTYPE)"
      ],
      "metadata": {
        "id": "i1a-BRoFN8Oc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import measured tree temperature data"
      ],
      "metadata": {
        "id": "ZPvCu0rVEaQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colnames_tree_temp = ['datetime', 's45_1', 'e9_1', 'n135_1','e45_2', 'n9_2', 'w135_2', 'n45_3', 'w9_3','s135_3', 'w_ext_35']\n",
        "url1 = \"https://raw.githubusercontent.com/yajuna/linearRegression/master/Tree_Temp_Values_AUG21_to_AUG28_2022.xlsx\"\n",
        "dataTemp = pandas.read_excel(url1,names=colnames_tree_temp)\n",
        "\n",
        "print(type(dataTemp))\n",
        "# dataTemp.datetime[2941:2943]"
      ],
      "metadata": {
        "id": "a-aVJtYoEZF0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ea303e9-5824-4be5-c4e8-39ca5c307a4b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## index counting and print to check:\n",
        "date = '08-21'\n",
        "\n",
        "if date == '08-21':\n",
        "# August 21:\n",
        "    [start, stop] = [0, 415]\n",
        "\n",
        "elif date == '08-22':\n",
        "# August 22:\n",
        "    [start, stop] = [415, 826]\n",
        "elif date == '08-23':\n",
        "# August 23:\n",
        "    [start, stop] = [826, 1253]\n",
        "elif date == '08-24':\n",
        "# August 24:\n",
        "    [start, stop] = [1253, 1677]\n",
        "elif date == '08-25':\n",
        "# August 25:\n",
        "    [start, stop] = [1677, 2107]\n",
        "elif date == '08-26':\n",
        "# August 26:\n",
        "    [start, stop] = [2107, 2503]\n",
        "elif date == '08-27':\n",
        "# August 27:\n",
        "    [start, stop] = [2503, 2942]\n",
        "elif date == '08-28':\n",
        "# August 28:\n",
        "    [start, stop] = [2942, 3379]"
      ],
      "metadata": {
        "id": "6REwSXkCx1JZ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tmin = 0.\n",
        "tmax = 24.\n",
        "\n",
        "N_temp = int(stop - start)\n",
        "\n",
        "t_temp = tf.linspace(tmin, tmax, N_temp)"
      ],
      "metadata": {
        "id": "7WfJ5-qgM4N7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# core temp is west, at 13.5cm, at 3m high\n",
        "s135_3 = np.array(dataTemp.s135_3[start:stop])\n",
        "# West, at 9cm, at 3m high\n",
        "w9_3 = np.array(dataTemp.w9_3[start:stop])\n",
        "# North, at 4.5cm, at 3m high\n",
        "n45_3 = np.array(dataTemp.n45_3[start:stop])\n",
        "# bark temp is West, at bark, at 3.5m high\n",
        "w_ext_35 = np.array(dataTemp.w_ext_35[start:stop])"
      ],
      "metadata": {
        "id": "7JBSgcxUx16A"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization\n",
        "\n",
        "Visualize the measured temperature data without any interpolation. Plot data of each day by inputing `[start,stop]` indices, then plot temperature data for the 8 days. Compute differences between each day and see if there is a pattern."
      ],
      "metadata": {
        "id": "dTuMcZD_LiRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = w9_3\n",
        "\n",
        "def namestr(obj, namespace):\n",
        "    return [name for name in namespace if namespace[name] is obj]\n",
        "\n",
        "n = namestr(temp, globals())\n",
        "print(n[0],'difference = (end of day - start of day) =', temp[-1] - temp[0])\n",
        "# plot train temp\n",
        "fig1, ax = plt.subplots()\n",
        "ax.plot(t_temp, temp)\n",
        "\n",
        "ax.set(xlabel='time (hrs)', ylabel='Temperature ($^{\\circ}C$)',\n",
        "       title='Temperature at '+n[0]+' in 24 hours')\n",
        "ax.set_ylim(20, 30)\n",
        "# # ax.grid()\n",
        "\n",
        "fig1.savefig(n[0] + date +\".eps\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "XJjmMvc6MzjO",
        "outputId": "69f740e3-9551-4e56-8a50-c5c14f4c6a39"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w9_3 difference = (end of day - start of day) = 0.3099999999999987\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import measured weather data"
      ],
      "metadata": {
        "id": "_rd_8ORwEf-5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colnames_weather = ['datetime', 'wind_speed', 'wind_direction', 'humidity', 'air_temperature', 'air_pressure', 'solar_DNI']\n",
        "url2 = \"https://raw.githubusercontent.com/yajuna/pinn_heat/master/Weather_Station_AUG21_to_AUG28_2022.xlsx\"\n",
        "dataWeather = pandas.read_excel(url2,names=colnames_weather)\n",
        "\n",
        "print(type(dataWeather))\n",
        "# dataWeather.datetime[432:433]"
      ],
      "metadata": {
        "id": "prjcDPj2EkYR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b887d97a-a996-4b35-d3c0-6b70cb12975c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# heat parameters\n",
        "sigma = 5.76e-8\n",
        "albedo = 0.3"
      ],
      "metadata": {
        "id": "A-DcOXxHUV5H"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## index counting and print to check\n",
        "if date == '08-21':\n",
        "# August 21:\n",
        "    [begin, end] = [0, 144]\n",
        "\n",
        "elif date == '08-22':\n",
        "# August 22:\n",
        "    [begin, end] = [144, 288]\n",
        "\n",
        "elif date == '08-23':\n",
        "# August 23:\n",
        "    [begin, end] = [288, 432]\n",
        "elif date == '08-24':\n",
        "# August 24:\n",
        "    [begin, end] = [432, 576]\n",
        "\n",
        "elif date == '08-25':\n",
        "# August 25:\n",
        "    [begin, end] = [576, 720]\n",
        "elif date == '08-26':\n",
        "# August 26:\n",
        "    [begin, end] = [720, 864]\n",
        "elif date == '08-27':\n",
        "# August 27:\n",
        "    [begin, end] = [864, 1008]\n",
        "elif date == '08-28':\n",
        "# August 28:\n",
        "    [begin, end] = [1008, 1152]"
      ],
      "metadata": {
        "id": "npzxS9c_yfuv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N_weather = int(end - begin)\n",
        "\n",
        "t_weather = tf.linspace(tmin, tmax, N_weather)"
      ],
      "metadata": {
        "id": "DuHYPISbUtTK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#### training weather data for source term\n",
        "# wind speed\n",
        "train_windSpeed = np.array(dataWeather.wind_speed[begin:end])\n",
        "# humidity\n",
        "train_humidity = np.array(dataWeather.humidity[begin:end])\n",
        "# air temperature\n",
        "train_airTemp = np.array(dataWeather.air_temperature[begin:end])\n",
        "# air pressure\n",
        "train_airPressure = np.array(dataWeather.air_pressure[begin:end])\n",
        "# solar radiation\n",
        "train_solar = np.array(dataWeather.solar_DNI[begin:end])"
      ],
      "metadata": {
        "id": "zpckkD42yjFh"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weather = train_windSpeed\n",
        "\n",
        "def namestr(obj, namespace):\n",
        "    return [name for name in namespace if namespace[name] is obj]\n",
        "\n",
        "nn = namestr(weather, globals())\n",
        "print(nn[0],'difference = (end of day - start of day) =', weather[-1] - weather[0])\n",
        "# plot weather\n",
        "fig1, ax = plt.subplots()\n",
        "ax.plot(t_weather, weather)\n",
        "\n",
        "ax.set(xlabel='time (hrs)', ylabel='weather',title='Plot of '+ nn[0] +' in 24 hours')\n",
        "# ax.set_ylim(lower_limit, upper_limit)\n",
        "# ax.grid()\n",
        "\n",
        "fig1.savefig(n[0] + date + \".eps\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "zHHvM774U80J",
        "outputId": "2eb16c3c-788b-4a92-e806-391758c88f35"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_windSpeed difference = (end of day - start of day) = 0.009099999999999664\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}