ipta_slope_analysis_method_lastedit_21_oct_2024.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1hnM2u2RBgQ6esCaaoDO7ES0hSAyPocgA",
      "authorship_tag": "ABX9TyN+M0P5hg5h/DCCODxnOP3z",
      "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/trewto/Enviroment_Study/blob/main/ipta_slope_analysis_method_lastedit_21_oct_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wd5FG3u9OioH"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel('/content/drive/MyDrive/research/Rainfall_1981-2022.xls', header=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: get all row from df_city  Where Month =1 , and devide it two part the first half and last half\n",
        "\n",
        "def convert_to_first_and_last_half(df_city):\n",
        "  dataset = []\n",
        "  # Filter rows where Month is 1\n",
        "  for i in range(1,13):\n",
        "    df_month = df_city[df_city['Month'] == i]\n",
        "\n",
        "    # Calculate the midpoint of the filtered DataFrame\n",
        "    midpoint = len(df_month) // 2\n",
        "\n",
        "    # Split the DataFrame into two parts\n",
        "    first_half = df_month.iloc[:midpoint]\n",
        "    last_half = df_month.iloc[midpoint:]\n",
        "\n",
        "    #print(\"First Half:\")\n",
        "    #print(first_half)\n",
        "    sum_first_half = first_half['sum_1_to_31'].sum()\n",
        "\n",
        "    sum_first_half = first_half['sum_1_to_31'].mean()\n",
        "\n",
        "    std_first_half = first_half['sum_1_to_31'].std()\n",
        "    #print(f\"The sum of 'sum_1_to_31' for the first half is: {sum_first_half}\")\n",
        "\n",
        "    #print(\"\\nLast Half:\")\n",
        "    #print(last_half)\n",
        "    sum_last_half = last_half['sum_1_to_31'].sum()\n",
        "    sum_last_half = last_half['sum_1_to_31'].mean()\n",
        "    std_last_half = last_half['sum_1_to_31'].std()\n",
        "    #print(f\"The sum of 'sum_1_to_31' for the last half is: {sum_last_half}\")\n",
        "    dataset.append([i, sum_first_half, sum_last_half,std_first_half,std_last_half])\n",
        "\n",
        "  df_dataset = pd.DataFrame(dataset, columns=['Month', 'Sum_First_Half', 'Sum_Last_Half','std_First_Half','std_Last_Half'])\n",
        "  print(df_dataset)\n",
        "  return df_dataset\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wPhXB_s_PSjE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to calculate slope between two points\n",
        "def calculate_slope(x1, y1, x2, y2):\n",
        "    if x2 - x1 != 0:\n",
        "        return (y2 - y1) / (x2 - x1)\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "def calculate_length(x1, y1, x2, y2):\n",
        "    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5\n",
        "\n",
        "\n",
        "\n",
        "# Function to calculate slopes and plot IPTA\n",
        "def plot_ipta(Month,data_first, data_last):\n",
        "    #data_first_1 will be in first collam Name Sum_First_Half and data_last one will be in second collam and merged\n",
        "    df = pd.concat([Month,data_first, data_last], axis=1)\n",
        "    df.columns = ['Month','Sum_First_Half', 'Sum_Last_Half']\n",
        "\n",
        "    # Calculate slopes and lengths\n",
        "    slopes = []\n",
        "    lengths= []\n",
        "\n",
        "\n",
        "    for i in range(len(df)):\n",
        "\n",
        "\n",
        "\n",
        "        if i ==  len(df) -1 :\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[0], df['Sum_Last_Half'].iloc[0]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "\n",
        "\n",
        "        else:\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[i + 1], df['Sum_Last_Half'].iloc[i + 1]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "    # Add None for the first slope (no previous point)\n",
        "    #slopes = [None] + slopes\n",
        "    slopes = slopes\n",
        "\n",
        "    # Add slopes to the DataFrame\n",
        "    df['Slopes'] = slopes\n",
        "    df['Length'] = lengths\n",
        "\n",
        "    # Plot IPTA\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(df['Sum_First_Half'], df['Sum_Last_Half'], marker='o', linestyle='--', color='b', label='Rainfall Data')\n",
        "\n",
        "    # Plot month names beside each point\n",
        "    for i in range(len(df)):\n",
        "        plt.text(df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i],\n",
        "                 f\"{df['Month'].iloc[i]}\", fontsize=9, ha='right')\n",
        "\n",
        "    # Plot slopes\n",
        "    #for i in range(len(df) - 1):\n",
        "    #    plt.text((df['Sum_First_Half'].iloc[i] + df['Sum_First_Half'].iloc[i + 1]) / 2,\n",
        "    #              (df['Sum_Last_Half'].iloc[i] + df['Sum_Last_Half'].iloc[i + 1]) / 2,\n",
        "     #             f\"Slope: {round(slopes[i + 1], 2)}\", fontsize=9, color='green')\n",
        "\n",
        "    # Plot 45-degree reference line\n",
        "    plt.plot([df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             [df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             'r--', label='45-degree line')\n",
        "\n",
        "    plt.title('IPTA Polygon Plot')\n",
        "    plt.xlabel('First Half  ')\n",
        "    plt.ylabel('Last Half  ')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    print(df)\n",
        "\n"
      ],
      "metadata": {
        "id": "WSY78RGfQwdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making Function CTWISE**"
      ],
      "metadata": {
        "id": "_CsrWN21SosY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "KdWSzf5nTCcg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/research/Rainfall_1981-2022.xls', header=0)"
      ],
      "metadata": {
        "id": "RfzseepUSrn6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:, 1:31] = df.loc[:, 1:31].apply(pd.to_numeric, errors='coerce')\n",
        "df['sum_1_to_31'] = df.loc[:, 1:31].sum(axis=1)\n",
        "#df.head()\n",
        "df = df[['Stati','Year', 'Month', 'sum_1_to_31']]"
      ],
      "metadata": {
        "id": "nqFNfaWBTUwM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_city = (df['Stati'].unique())"
      ],
      "metadata": {
        "id": "ZrecJ4NTS2HK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "for city in unique_city:\n",
        "    if city in ['          Daily  total  Rainfall  data  in  Millimeter  ', 'Stati', np.nan]:\n",
        "        continue\n",
        "    print (i,city)\n",
        "\n",
        "    df_city = df[df['Stati'] == city]\n",
        "    df_city = df_city[['Year', 'Month', 'sum_1_to_31']]\n",
        "\n",
        "\n",
        "\n",
        "    df_dataset = convert_to_first_and_last_half(df_city)\n",
        "\n",
        "    plot_ipta(df_dataset['Month'],df_dataset['Sum_First_Half'],df_dataset['Sum_Last_Half'])\n",
        "\n",
        "\n",
        "    print( \"Plot Rainfall std\")\n",
        "    plot_ipta(df_dataset['Month'],df_dataset['std_First_Half'],df_dataset['std_Last_Half'])\n",
        "\n",
        "    print(\"========================\")\n",
        "    i= i+1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 827
        },
        "id": "Vpvwdp44S9h_",
        "outputId": "600c27c7-ebac-4690-ccfb-9bded002f863"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Month  Sum_First_Half  Sum_Last_Half    Slopes      Length\n",
            "0       1       10.843914      12.019231  2.373150   54.659916\n",
            "1       2       32.069122      62.389827 -0.394086   62.731387\n",
            "2       3       90.432006      39.389810  1.986017   75.464966\n",
            "3       4      124.370663     106.792545  0.046988   82.856749\n",
            "4       5      207.136095     110.681525 -7.030652  155.343623\n",
            "5       6      185.261064     264.477247  0.822723  176.025712\n",
            "6       7      321.194430     376.312712  6.604774  123.042524\n",
            "7       8      302.775023     254.656688  0.149362  158.946327\n",
            "8       9      145.572531     231.176659 -0.949995  130.638334\n",
            "9      10      240.285485     141.199825  0.610402  193.764426\n",
            "10     11       74.897676      40.246739  0.362112   62.957360\n",
            "11     12       15.701835      18.811218  1.398126    8.350478\n",
            "========================\n"
          ]
        }
      ]
    }
  ]
}