feildspec_derivative.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kvy4aIFt5-5z",
        "outputId": "abbe9641-493b-45b9-b16b-8462f0735653"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        " ''' The code interpolates the 1nm reflectance to 10nm\n",
        " and computes derivatives'''\n",
        "\n",
        " #importing packages\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" We want to load the excel book and call each weeksheet.\n",
        "This saves us time from saving each worksheet seperately\n",
        "\"\"\"\n",
        "\n",
        "ZachA_LynnW = pd.ExcelFile('/content/042623_HWHF_10d_1_1.xlsx')\n",
        "ZachA_15_Weeds = pd.read_excel(ZachA_LynnW, 'ZachA_15_Weeds')\n",
        "ZachA_15_Weeds['Mean Reflectance'] = ZachA_15_Weeds.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_15_Soil = pd.read_excel(ZachA_LynnW, 'ZachA15_Soil')\n",
        "ZachA_15_Soil['Mean Reflectance'] = ZachA_15_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_14_Weeds = pd.read_excel(ZachA_LynnW, 'ZachA_14_Weeds')\n",
        "ZachA_14_Weeds['Mean Reflectance'] = ZachA_14_Weeds.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_14_Soil = pd.read_excel(ZachA_LynnW, 'ZachA14_Soil')\n",
        "ZachA_14_Soil['Mean Reflectance'] = ZachA_14_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_13_Weeds = pd.read_excel(ZachA_LynnW, 'ZachA13_Weeds')\n",
        "ZachA_13_Weeds['Mean Reflectance'] = ZachA_13_Weeds.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_13_Soil = pd.read_excel(ZachA_LynnW, 'ZachA13_10D_Soil')\n",
        "ZachA_13_Soil['Mean Reflectance'] = ZachA_13_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_12_Wheat = pd.read_excel(ZachA_LynnW, 'ZachA12_Wheat')\n",
        "ZachA_12_Wheat['Mean Reflectance'] = ZachA_12_Wheat.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_12_Road = pd.read_excel(ZachA_LynnW, 'ZachA12_Road')\n",
        "ZachA_12_Road['Mean Reflectance'] = ZachA_12_Road.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_11_Wheat = pd.read_excel(ZachA_LynnW, 'ZackA_11_Wheat')\n",
        "ZachA_11_Wheat['Mean Reflectance'] = ZachA_11_Wheat.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "ZachA_11_Soil = pd.read_excel(ZachA_LynnW, 'ZachA11_Soil')\n",
        "ZachA_11_Soil['Mean Reflectance'] = ZachA_11_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW05_CornRes = pd.read_excel(ZachA_LynnW, '042623_LynnW05_Cornres')\n",
        "LynnW05_CornRes['Mean Reflectance'] = LynnW05_CornRes.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW05_Soil = pd.read_excel(ZachA_LynnW, '042623_LynnW05_Bare_Soil')\n",
        "LynnW05_Soil['Mean Reflectance'] = LynnW05_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW04_CornRes = pd.read_excel(ZachA_LynnW, '042623_LynnW04_Cornres')\n",
        "LynnW04_CornRes['Mean Reflectance'] = LynnW04_CornRes.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW04_Soil = pd.read_excel(ZachA_LynnW, '042623LynnW04_Bare_SOil')\n",
        "LynnW04_Soil['Mean Reflectance'] = LynnW04_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW03_CornRes = pd.read_excel(ZachA_LynnW, '042623_LynnW03_Cornres')\n",
        "LynnW03_CornRes['Mean Reflectance'] = LynnW03_CornRes.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW03_Soil = pd.read_excel(ZachA_LynnW, '042623LynnW_03_Bare_Soil')\n",
        "LynnW03_Soil['Mean Reflectance'] = LynnW03_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW02_CornRes = pd.read_excel(ZachA_LynnW, '042623_LynnW02_Cornres')\n",
        "LynnW02_CornRes['Mean Reflectance'] = LynnW02_CornRes.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW02_Soil = pd.read_excel(ZachA_LynnW, '042623_LynnW02_Bare_Soil')\n",
        "LynnW02_Soil['Mean Reflectance'] = LynnW02_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW01_CornRes = pd.read_excel(ZachA_LynnW, '042623_LynnW01_Cornres')\n",
        "LynnW01_CornRes['Mean Reflectance'] = LynnW01_CornRes.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "LynnW01_Soil = pd.read_excel(ZachA_LynnW, '042623_LynnW01_Bare_Soil')\n",
        "LynnW01_Soil['Mean Reflectance'] = LynnW01_Soil.drop('Wavelength', axis=1).mean(axis=1)*100\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8c3sgkZAcu9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''We want to  plot the average reflectance\n",
        "for 1nm wavelength. This was plotted after the atmospheric corrections\n",
        "had been done in excel\n",
        "'''\n",
        "fig,axs = plt.subplots(2,2, figsize =(10,10))\n",
        "axs[0,0].plot(ZachA_15_Weeds['Wavelength'], ZachA_15_Weeds['Mean Reflectance'])\n",
        "axs[0,0].plot(ZachA_14_Weeds['Wavelength'], ZachA_14_Weeds['Mean Reflectance'])\n",
        "axs[0,0].plot(ZachA_13_Weeds['Wavelength'], ZachA_13_Weeds['Mean Reflectance'])\n",
        "axs[0,0].plot(ZachA_12_Wheat['Wavelength'], ZachA_12_Wheat['Mean Reflectance'])\n",
        "axs[0,0].plot(ZachA_11_Wheat['Wavelength'], ZachA_11_Wheat['Mean Reflectance'])\n",
        "axs[0,0].plot(ZachA_12_Road['Wavelength'], ZachA_12_Road['Mean Reflectance'])\n",
        "axs[0,0].set_title(\"Zach A Farms - Weeds: April 26, 2023\")\n",
        "axs[0,0].set_xlabel('Wavelength')\n",
        "axs[0,0].set_ylabel('Reflectance')\n",
        "\n",
        "axs[0,1].plot(ZachA_15_Soil['Wavelength'], ZachA_15_Soil['Mean Reflectance'])\n",
        "axs[0,1].plot(ZachA_14_Soil['Wavelength'], ZachA_14_Soil['Mean Reflectance'])\n",
        "axs[0,1].plot(ZachA_13_Soil['Wavelength'], ZachA_13_Soil['Mean Reflectance'])\n",
        "axs[0,1].plot(ZachA_11_Soil['Wavelength'], ZachA_11_Soil['Mean Reflectance'])\n",
        "axs[0,1].set_title(\"Zach A Farms - Bare Soil: April 26, 2023\")\n",
        "axs[0,1].set_xlabel('Wavelength')\n",
        "axs[0,1].set_ylabel('Reflectance')\n",
        "\n",
        "\n",
        "axs[1,0].plot(LynnW05_Soil['Wavelength'], LynnW05_Soil['Mean Reflectance'])\n",
        "axs[1,0].plot(LynnW04_Soil['Wavelength'], LynnW04_Soil['Mean Reflectance'])\n",
        "axs[1,0].plot(LynnW03_Soil['Wavelength'], LynnW03_Soil['Mean Reflectance'])\n",
        "axs[1,0].plot(LynnW02_Soil['Wavelength'], LynnW02_Soil['Mean Reflectance'])\n",
        "axs[1,0].plot(LynnW01_Soil['Wavelength'], LynnW01_Soil['Mean Reflectance'])\n",
        "axs[1,0].set_title(\"Lynn W Farms - Bare Soil: April 26, 2023\")\n",
        "axs[1,0].set_xlabel('Wavelength')\n",
        "axs[1,0].set_ylabel('Reflectance')\n",
        "\n",
        "axs[1,1].plot(LynnW05_CornRes['Wavelength'], LynnW05_CornRes['Mean Reflectance'])\n",
        "axs[1,1].plot(LynnW04_CornRes['Wavelength'], LynnW04_CornRes['Mean Reflectance'])\n",
        "axs[1,1].plot(LynnW03_CornRes['Wavelength'], LynnW03_CornRes['Mean Reflectance'])\n",
        "axs[1,1].plot(LynnW02_CornRes['Wavelength'], LynnW02_CornRes['Mean Reflectance'])\n",
        "axs[1,1].plot(LynnW01_CornRes['Wavelength'], LynnW01_CornRes['Mean Reflectance'])\n",
        "axs[1,1].set_title(\"Lynn W Farms - Corn Residue: April 26, 2023\")\n",
        "axs[1,1].set_xlabel('Wavelength')\n",
        "axs[1,1].set_ylabel('Reflectance')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "88skNnDkvhdT",
        "outputId": "6c0d49ff-a24e-498a-929f-0c512c7c8ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Reflectance')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "giAW42IcnnEZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kNSTwY0Q7dMk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bTx-K8RE77F3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" We want to create a numpy array of the Mean Reflectances\n",
        "\"\"\"\n",
        "xvals = np.array(range(350, 2501, 10))\n",
        "Wavelength_np = np.array(ZachA_15_Weeds[\"Wavelength\"])\n",
        "Wavelength_np_LynnW = np.array(LynnW05_CornRes[\"Wavelength\"])\n",
        "\n",
        "ZachA_15_Weeds_np= np.array(ZachA_15_Weeds['Mean Reflectance'])\n",
        "ZachA_15_Soil_np = np.array(ZachA_15_Soil['Mean Reflectance'])\n",
        "ZachA_14_Weeds_np= np.array(ZachA_14_Weeds['Mean Reflectance'])\n",
        "ZachA_14_Soil_np = np.array(ZachA_14_Soil['Mean Reflectance'])\n",
        "ZachA_13_Weeds_np= np.array(ZachA_13_Weeds['Mean Reflectance'])\n",
        "ZachA_13_Soil_np = np.array(ZachA_13_Soil['Mean Reflectance'])\n",
        "ZachA_12_Wheat_np= np.array(ZachA_12_Wheat['Mean Reflectance'])\n",
        "ZachA_12_Road_np = np.array(ZachA_12_Road['Mean Reflectance'])\n",
        "ZachA_11_Wheat_np= np.array(ZachA_11_Wheat['Mean Reflectance'])\n",
        "ZachA_11_Soil_np = np.array(ZachA_11_Soil['Mean Reflectance'])\n",
        "\n",
        "LynnW05_CornRes_np = np.array( LynnW05_CornRes['Mean Reflectance'])\n",
        "LynnW05_Soil_np = np.array (LynnW05_Soil['Mean Reflectance'])\n",
        "LynnW04_CornRes_np = np.array( LynnW04_CornRes['Mean Reflectance'])\n",
        "LynnW04_Soil_np = np.array (LynnW04_Soil['Mean Reflectance'])\n",
        "LynnW03_CornRes_np = np.array( LynnW03_CornRes['Mean Reflectance'])\n",
        "LynnW03_Soil_np = np.array (LynnW03_Soil['Mean Reflectance'])\n",
        "LynnW02_CornRes_np = np.array( LynnW02_CornRes['Mean Reflectance'])\n",
        "LynnW02_Soil_np = np.array (LynnW02_Soil['Mean Reflectance'])\n",
        "LynnW01_CornRes_np = np.array( LynnW01_CornRes['Mean Reflectance'])\n",
        "LynnW01_Soil_np = np.array (LynnW01_Soil['Mean Reflectance'])"
      ],
      "metadata": {
        "id": "NzqzxZ6rxLix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"We wanted to confirm the length of these columns are uniform\"\n",
        "\n",
        "print(Wavelength_np_LynnW.size)\n",
        "print(LynnW04_CornRes_np.size)\n",
        "print(LynnW05_Soil_np.size)\n",
        "print(LynnW04_Soil_np.size)\n",
        "print(LynnW02_CornRes_np.size)\n",
        "print(LynnW01_CornRes_np.size)\n",
        "\n",
        "print(LynnW04_Soil['Mean Reflectance'] .size)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9A5Bb2JJzO-2",
        "outputId": "ee0ebda7-2e4a-4e7a-90ed-f57a2df8960b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1923\n",
            "1923\n",
            "1923\n",
            "1923\n",
            "1923\n",
            "1923\n",
            "1923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"We want to calculate the interpolations for all samples using the numpy interpolation function\n",
        "\"\"\"\n",
        "ZachA_15_Weeds_10nm = np.interp(xvals,Wavelength_np,ZachA_15_Weeds_np)\n",
        "ZachA_15_Soil_10nm = np.interp(xvals,Wavelength_np,ZachA_15_Soil_np)\n",
        "ZachA_14_Weeds_10nm = np.interp(xvals,Wavelength_np,ZachA_14_Weeds_np)\n",
        "ZachA_14_Soil_10nm = np.interp(xvals,Wavelength_np,ZachA_14_Soil_np)\n",
        "ZachA_13_Weeds_10nm = np.interp(xvals,Wavelength_np,ZachA_13_Weeds_np)\n",
        "ZachA_13_Soil_10nm = np.interp(xvals,Wavelength_np,ZachA_13_Soil_np)\n",
        "ZachA_12_Wheat_10nm = np.interp(xvals,Wavelength_np,ZachA_12_Wheat_np)\n",
        "ZachA_12_Road_10nm = np.interp(xvals,Wavelength_np,ZachA_12_Road_np)\n",
        "ZachA_11_Wheat_10nm = np.interp(xvals,Wavelength_np,ZachA_11_Wheat_np)\n",
        "ZachA_11_Soil_10nm = np.interp(xvals,Wavelength_np,ZachA_11_Soil_np)\n",
        "\n",
        "LynnW_05_CornRes_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW05_CornRes_np)\n",
        "LynnW_05_Soil_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW05_Soil_np)\n",
        "LynnW_04_CornRes_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW04_CornRes_np)\n",
        "LynnW_04_Soil_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW04_Soil_np)\n",
        "LynnW_03_CornRes_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW03_CornRes_np)\n",
        "LynnW_03_Soil_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW03_Soil_np)\n",
        "LynnW_02_CornRes_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW02_CornRes_np )\n",
        "LynnW_02_Soil_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW02_Soil_np)\n",
        "LynnW_01_CornRes_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW01_CornRes_np)\n",
        "LynnW_01_Soil_10nm = np.interp(xvals,Wavelength_np_LynnW,LynnW01_Soil_np)\n",
        "\n"
      ],
      "metadata": {
        "id": "L6IoqH62Eok6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "''' we want to claculate the derivative using the gradient function\n",
        "dydx = np.gradient(y,x)\n",
        "'''\n",
        "\n",
        "\n",
        "ZachA_15_Weeds_derivative = np.gradient(ZachA_15_Weeds_10nm , xvals)\n",
        "ZachA_15_Soil_derivative = np.gradient(ZachA_15_Soil_10nm , xvals)\n",
        "ZachA_14_Weeds_derivative = np.gradient(ZachA_14_Weeds_10nm , xvals)\n",
        "ZachA_14_Soil_derivative = np.gradient(ZachA_14_Soil_10nm , xvals)\n",
        "ZachA_13_Weeds_derivative = np.gradient(ZachA_13_Weeds_10nm , xvals)\n",
        "ZachA_13_Soil_derivative = np.gradient(ZachA_13_Soil_10nm , xvals)\n",
        "ZachA_12_Wheat_derivative = np.gradient(ZachA_12_Wheat_10nm , xvals)\n",
        "ZachA_12_Road_derivative = np.gradient(ZachA_12_Road_10nm , xvals)\n",
        "ZachA_11_Wheat_derivative = np.gradient(ZachA_11_Wheat_10nm , xvals)\n",
        "ZachA_11_Soil_derivative = np.gradient(ZachA_11_Soil_10nm , xvals)\n",
        "\n",
        "\n",
        "LynnW_05_CornRes_derivative = np.gradient(LynnW_05_CornRes_10nm , xvals)\n",
        "LynnW_05_Soil_derivative = np.gradient(LynnW_05_Soil_10nm , xvals)\n",
        "LynnW_04_CornRes_derivative = np.gradient(LynnW_04_CornRes_10nm , xvals)\n",
        "LynnW_04_Soil_derivative = np.gradient(LynnW_04_Soil_10nm , xvals)\n",
        "LynnW_03_CornRes_derivative = np.gradient(LynnW_03_CornRes_10nm , xvals)\n",
        "LynnW_03_Soil_derivative = np.gradient(LynnW_03_Soil_10nm , xvals)\n",
        "LynnW_02_CornRes_derivative = np.gradient(LynnW_02_CornRes_10nm , xvals)\n",
        "LynnW_02_Soil_derivative = np.gradient(LynnW_02_Soil_10nm , xvals)\n",
        "LynnW_01_CornRes_derivative = np.gradient(LynnW_01_CornRes_10nm , xvals)\n",
        "LynnW_01_Soil_derivative = np.gradient(LynnW_01_Soil_10nm , xvals)\n",
        "\n"
      ],
      "metadata": {
        "id": "xUwDJtTN_tCY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"We want to create a dataframe for derivative of Zach A farms\"\"\"\n",
        "\n",
        "derivative_ZachA = pd.DataFrame({'Wavelength':xvals,\n",
        "                                       'ZachA_15_Weeds_derivative': ZachA_15_Weeds_derivative,\n",
        "                                       'ZachA_15_Soil_derivative': ZachA_15_Soil_derivative,\n",
        "                                       'ZachA_14_Weeds_derivative': ZachA_14_Weeds_derivative,\n",
        "                                       'ZachA_14_Soil_derivative':ZachA_14_Soil_derivative,\n",
        "                                       'ZachA_13_Weeds_derivative':ZachA_13_Weeds_derivative,\n",
        "                                       'ZachA_13_Soil_derivative': ZachA_13_Soil_derivative,\n",
        "                                       'ZachA_12_Wheat_derivative ':ZachA_12_Wheat_derivative ,\n",
        "                                       'ZachA_12_Road_derivative': ZachA_12_Road_derivative,\n",
        "                                       'ZachA_11_Wheat_derivative':ZachA_11_Wheat_derivative,\n",
        "                                       'ZachA_11_Soil_derivative': ZachA_11_Soil_derivative\n",
        "                                       } )"
      ],
      "metadata": {
        "id": "ZVZTnXVLhmSD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" We want to create a dataframe for derivatives of LynnW Farms\n",
        "\"\"\"\n",
        "derivative_LynnW = pd.DataFrame({'Wavelength':xvals,\n",
        "                                 'LynnW_05_CornRes_derivative': LynnW_05_CornRes_derivative,\n",
        "                                 'LynnW_05_Soil_derivative': LynnW_05_Soil_derivative,\n",
        "                                 'LynnW_04_CornRes_derivative ': LynnW_04_CornRes_derivative ,\n",
        "                                 'LynnW_04_Soil_derivative ': LynnW_04_Soil_derivative,\n",
        "                                 'LynnW_03_CornRes_derivative ': LynnW_03_CornRes_derivative,\n",
        "                                 'LynnW_03_Soil_derivative':LynnW_03_Soil_derivative,\n",
        "                                 'LynnW_02_CornRes_derivative ': LynnW_02_CornRes_derivative,\n",
        "                                 'LynnW_02_Soil_derivative':LynnW_02_Soil_derivative,\n",
        "                                 'LynnW_01_CornRes_derivative ': LynnW_01_CornRes_derivative,\n",
        "                                 'LynnW_01_Soil_derivative': LynnW_01_Soil_derivative,\n",
        "                          } )\n"
      ],
      "metadata": {
        "id": "MwzGCPYbRkg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"We want to create a dataframe for all derivatives\n",
        " for ZachA and LynnW Farms\n",
        " \"\"\"\n",
        "derivative_ZachA_LynnW = pd.DataFrame({'Wavelength':xvals,\n",
        "                                       'ZachA_15_Weeds_derivative': ZachA_15_Weeds_derivative,\n",
        "                                       'ZachA_15_Soil_derivative': ZachA_15_Soil_derivative,\n",
        "                                       'ZachA_14_Weeds_derivative': ZachA_14_Weeds_derivative,\n",
        "                                       'ZachA_14_Soil_derivative':ZachA_14_Soil_derivative,\n",
        "                                       'ZachA_13_Weeds_derivative':ZachA_13_Weeds_derivative,\n",
        "                                       'ZachA_13_Soil_derivative': ZachA_13_Soil_derivative,\n",
        "                                       'ZachA_12_Wheat_derivative ':ZachA_12_Wheat_derivative ,\n",
        "                                       'ZachA_12_Road_derivative': ZachA_12_Road_derivative,\n",
        "                                       'ZachA_11_Wheat_derivative':ZachA_11_Wheat_derivative,\n",
        "                                       'ZachA_11_Soil_derivative': ZachA_11_Soil_derivative,\n",
        "                                       'LynnW_05_CornRes_derivative': LynnW_05_CornRes_derivative,\n",
        "                                       'LynnW_05_Soil_derivative': LynnW_05_Soil_derivative,\n",
        "                                       'LynnW_04_CornRes_derivative ': LynnW_04_CornRes_derivative ,\n",
        "                                       'LynnW_04_Soil_derivative ': LynnW_04_Soil_derivative,\n",
        "                                       'LynnW_03_CornRes_derivative ': LynnW_03_CornRes_derivative,\n",
        "                                       'LynnW_03_Soil_derivative':LynnW_03_Soil_derivative,\n",
        "                                       'LynnW_02_CornRes_derivative ': LynnW_02_CornRes_derivative,\n",
        "                                       'LynnW_02_Soil_derivative':LynnW_02_Soil_derivative,\n",
        "                                       'LynnW_01_CornRes_derivative ': LynnW_01_CornRes_derivative,\n",
        "                                       'LynnW_01_Soil_derivative': LynnW_01_Soil_derivative,\n",
        "                                       } )"
      ],
      "metadata": {
        "id": "pU6t8QMaj3t5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"We want to show the first 10 rows of the derivatives\"\"\"\n",
        "derivative_ZachA_LynnW .head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "Tabl3fWfkhAk",
        "outputId": "aecbdff7-adc0-485e-bc77-1bdaa9739729"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Wavelength  ZachA_15_Weeds_derivative  ZachA_15_Soil_derivative  \\\n",
              "0         350                  -0.001065                  0.012294   \n",
              "1         360                   0.001130                  0.016511   \n",
              "2         370                   0.007323                  0.029494   \n",
              "3         380                   0.015395                  0.046133   \n",
              "4         390                   0.023278                  0.053879   \n",
              "5         400                   0.033142                  0.050415   \n",
              "6         410                   0.049315                  0.052549   \n",
              "7         420                   0.060517                  0.063444   \n",
              "8         430                   0.053286                  0.070526   \n",
              "9         440                   0.036665                  0.061641   \n",
              "\n",
              "   ZachA_14_Weeds_derivative  ZachA_14_Soil_derivative  \\\n",
              "0                  -0.000208                  0.008234   \n",
              "1                  -0.000905                  0.009922   \n",
              "2                   0.003313                  0.021173   \n",
              "3                   0.012361                  0.037760   \n",
              "4                   0.017802                  0.042840   \n",
              "5                   0.025602                  0.039731   \n",
              "6                   0.041659                  0.043123   \n",
              "7                   0.049392                  0.053473   \n",
              "8                   0.039808                  0.061577   \n",
              "9                   0.024979                  0.055165   \n",
              "\n",
              "   ZachA_13_Weeds_derivative  ZachA_13_Soil_derivative  \\\n",
              "0                  -0.002348                  0.001118   \n",
              "1                   0.001336                  0.005548   \n",
              "2                   0.007999                  0.016265   \n",
              "3                   0.015365                  0.027891   \n",
              "4                   0.022536                  0.033465   \n",
              "5                   0.027663                  0.030971   \n",
              "6                   0.036921                  0.032339   \n",
              "7                   0.044197                  0.041027   \n",
              "8                   0.040209                  0.046623   \n",
              "9                   0.030056                  0.040384   \n",
              "\n",
              "   ZachA_12_Wheat_derivative   ZachA_12_Road_derivative  \\\n",
              "0                   -0.003749                 -0.002886   \n",
              "1                   -0.004304                 -0.003252   \n",
              "2                   -0.001632                 -0.000358   \n",
              "3                    0.003418                  0.006733   \n",
              "4                    0.006135                  0.012706   \n",
              "5                    0.011532                  0.024991   \n",
              "6                    0.021660                  0.049590   \n",
              "7                    0.028394                  0.056959   \n",
              "8                    0.025096                  0.036639   \n",
              "9                    0.017363                  0.014928   \n",
              "\n",
              "   ZachA_11_Wheat_derivative  ...  LynnW_05_CornRes_derivative  \\\n",
              "0                  -0.163836  ...                     0.013733   \n",
              "1                   0.010595  ...                     0.016022   \n",
              "2                  -0.030939  ...                     0.022457   \n",
              "3                  -0.431450  ...                     0.031614   \n",
              "4                  -0.586099  ...                     0.039016   \n",
              "5                  -0.624610  ...                     0.039508   \n",
              "6                  -0.732561  ...                     0.042248   \n",
              "7                  -0.750094  ...                     0.048415   \n",
              "8                  -0.611048  ...                     0.049428   \n",
              "9                  -0.424764  ...                     0.045688   \n",
              "\n",
              "   LynnW_05_Soil_derivative  LynnW_04_CornRes_derivative   \\\n",
              "0                  0.007118                      0.017358   \n",
              "1                  0.007402                      0.021112   \n",
              "2                  0.013675                      0.032752   \n",
              "3                  0.024150                      0.048067   \n",
              "4                  0.027867                      0.059619   \n",
              "5                  0.026105                      0.064177   \n",
              "6                  0.027942                      0.072569   \n",
              "7                  0.033778                      0.083678   \n",
              "8                  0.037921                      0.085859   \n",
              "9                  0.035198                      0.079430   \n",
              "\n",
              "   LynnW_04_Soil_derivative   LynnW_03_CornRes_derivative   \\\n",
              "0                   0.003895                      0.016674   \n",
              "1                   0.007097                      0.017535   \n",
              "2                   0.014942                      0.023535   \n",
              "3                   0.024215                      0.035927   \n",
              "4                   0.028634                      0.047661   \n",
              "5                   0.026182                      0.053738   \n",
              "6                   0.026920                      0.062272   \n",
              "7                   0.033630                      0.071367   \n",
              "8                   0.038542                      0.071130   \n",
              "9                   0.034602                      0.065149   \n",
              "\n",
              "   LynnW_03_Soil_derivative  LynnW_02_CornRes_derivative   \\\n",
              "0                  0.002874                      0.004143   \n",
              "1                  0.006391                      0.008193   \n",
              "2                  0.013620                      0.015076   \n",
              "3                  0.021268                      0.022728   \n",
              "4                  0.024967                      0.029285   \n",
              "5                  0.022385                      0.032058   \n",
              "6                  0.022780                      0.038004   \n",
              "7                  0.028768                      0.043587   \n",
              "8                  0.033154                      0.042851   \n",
              "9                  0.029286                      0.039552   \n",
              "\n",
              "   LynnW_02_Soil_derivative  LynnW_01_CornRes_derivative   \\\n",
              "0                  0.005016                      0.031518   \n",
              "1                  0.007888                      0.030193   \n",
              "2                  0.015186                      0.038761   \n",
              "3                  0.023773                      0.061830   \n",
              "4                  0.028019                      0.080384   \n",
              "5                  0.025461                      0.078998   \n",
              "6                  0.026454                      0.082085   \n",
              "7                  0.032701                      0.095549   \n",
              "8                  0.035991                      0.098564   \n",
              "9                  0.033156                      0.089842   \n",
              "\n",
              "   LynnW_01_Soil_derivative  \n",
              "0                  0.016664  \n",
              "1                  0.016364  \n",
              "2                  0.026616  \n",
              "3                  0.048665  \n",
              "4                  0.062202  \n",
              "5                  0.057073  \n",
              "6                  0.056733  \n",
              "7                  0.069169  \n",
              "8                  0.077181  \n",
              "9                  0.069167  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa4434bd-dcfc-459d-a24a-a22230c7973c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Wavelength</th>\n",
              "      <th>ZachA_15_Weeds_derivative</th>\n",
              "      <th>ZachA_15_Soil_derivative</th>\n",
              "      <th>ZachA_14_Weeds_derivative</th>\n",
              "      <th>ZachA_14_Soil_derivative</th>\n",
              "      <th>ZachA_13_Weeds_derivative</th>\n",
              "      <th>ZachA_13_Soil_derivative</th>\n",
              "      <th>ZachA_12_Wheat_derivative</th>\n",
              "      <th>ZachA_12_Road_derivative</th>\n",
              "      <th>ZachA_11_Wheat_derivative</th>\n",
              "      <th>...</th>\n",
              "      <th>LynnW_05_CornRes_derivative</th>\n",
              "      <th>LynnW_05_Soil_derivative</th>\n",
              "      <th>LynnW_04_CornRes_derivative</th>\n",
              "      <th>LynnW_04_Soil_derivative</th>\n",
              "      <th>LynnW_03_CornRes_derivative</th>\n",
              "      <th>LynnW_03_Soil_derivative</th>\n",
              "      <th>LynnW_02_CornRes_derivative</th>\n",
              "      <th>LynnW_02_Soil_derivative</th>\n",
              "      <th>LynnW_01_CornRes_derivative</th>\n",
              "      <th>LynnW_01_Soil_derivative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>350</td>\n",
              "      <td>-0.001065</td>\n",
              "      <td>0.012294</td>\n",
              "      <td>-0.000208</td>\n",
              "      <td>0.008234</td>\n",
              "      <td>-0.002348</td>\n",
              "      <td>0.001118</td>\n",
              "      <td>-0.003749</td>\n",
              "      <td>-0.002886</td>\n",
              "      <td>-0.163836</td>\n",
              "      <td>...</td>\n",
              "      <td>0.013733</td>\n",
              "      <td>0.007118</td>\n",
              "      <td>0.017358</td>\n",
              "      <td>0.003895</td>\n",
              "      <td>0.016674</td>\n",
              "      <td>0.002874</td>\n",
              "      <td>0.004143</td>\n",
              "      <td>0.005016</td>\n",
              "      <td>0.031518</td>\n",
              "      <td>0.016664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>360</td>\n",
              "      <td>0.001130</td>\n",
              "      <td>0.016511</td>\n",
              "      <td>-0.000905</td>\n",
              "      <td>0.009922</td>\n",
              "      <td>0.001336</td>\n",
              "      <td>0.005548</td>\n",
              "      <td>-0.004304</td>\n",
              "      <td>-0.003252</td>\n",
              "      <td>0.010595</td>\n",
              "      <td>...</td>\n",
              "      <td>0.016022</td>\n",
              "      <td>0.007402</td>\n",
              "      <td>0.021112</td>\n",
              "      <td>0.007097</td>\n",
              "      <td>0.017535</td>\n",
              "      <td>0.006391</td>\n",
              "      <td>0.008193</td>\n",
              "      <td>0.007888</td>\n",
              "      <td>0.030193</td>\n",
              "      <td>0.016364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>370</td>\n",
              "      <td>0.007323</td>\n",
              "      <td>0.029494</td>\n",
              "      <td>0.003313</td>\n",
              "      <td>0.021173</td>\n",
              "      <td>0.007999</td>\n",
              "      <td>0.016265</td>\n",
              "      <td>-0.001632</td>\n",
              "      <td>-0.000358</td>\n",
              "      <td>-0.030939</td>\n",
              "      <td>...</td>\n",
              "      <td>0.022457</td>\n",
              "      <td>0.013675</td>\n",
              "      <td>0.032752</td>\n",
              "      <td>0.014942</td>\n",
              "      <td>0.023535</td>\n",
              "      <td>0.013620</td>\n",
              "      <td>0.015076</td>\n",
              "      <td>0.015186</td>\n",
              "      <td>0.038761</td>\n",
              "      <td>0.026616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>380</td>\n",
              "      <td>0.015395</td>\n",
              "      <td>0.046133</td>\n",
              "      <td>0.012361</td>\n",
              "      <td>0.037760</td>\n",
              "      <td>0.015365</td>\n",
              "      <td>0.027891</td>\n",
              "      <td>0.003418</td>\n",
              "      <td>0.006733</td>\n",
              "      <td>-0.431450</td>\n",
              "      <td>...</td>\n",
              "      <td>0.031614</td>\n",
              "      <td>0.024150</td>\n",
              "      <td>0.048067</td>\n",
              "      <td>0.024215</td>\n",
              "      <td>0.035927</td>\n",
              "      <td>0.021268</td>\n",
              "      <td>0.022728</td>\n",
              "      <td>0.023773</td>\n",
              "      <td>0.061830</td>\n",
              "      <td>0.048665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>390</td>\n",
              "      <td>0.023278</td>\n",
              "      <td>0.053879</td>\n",
              "      <td>0.017802</td>\n",
              "      <td>0.042840</td>\n",
              "      <td>0.022536</td>\n",
              "      <td>0.033465</td>\n",
              "      <td>0.006135</td>\n",
              "      <td>0.012706</td>\n",
              "      <td>-0.586099</td>\n",
              "      <td>...</td>\n",
              "      <td>0.039016</td>\n",
              "      <td>0.027867</td>\n",
              "      <td>0.059619</td>\n",
              "      <td>0.028634</td>\n",
              "      <td>0.047661</td>\n",
              "      <td>0.024967</td>\n",
              "      <td>0.029285</td>\n",
              "      <td>0.028019</td>\n",
              "      <td>0.080384</td>\n",
              "      <td>0.062202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>400</td>\n",
              "      <td>0.033142</td>\n",
              "      <td>0.050415</td>\n",
              "      <td>0.025602</td>\n",
              "      <td>0.039731</td>\n",
              "      <td>0.027663</td>\n",
              "      <td>0.030971</td>\n",
              "      <td>0.011532</td>\n",
              "      <td>0.024991</td>\n",
              "      <td>-0.624610</td>\n",
              "      <td>...</td>\n",
              "      <td>0.039508</td>\n",
              "      <td>0.026105</td>\n",
              "      <td>0.064177</td>\n",
              "      <td>0.026182</td>\n",
              "      <td>0.053738</td>\n",
              "      <td>0.022385</td>\n",
              "      <td>0.032058</td>\n",
              "      <td>0.025461</td>\n",
              "      <td>0.078998</td>\n",
              "      <td>0.057073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>410</td>\n",
              "      <td>0.049315</td>\n",
              "      <td>0.052549</td>\n",
              "      <td>0.041659</td>\n",
              "      <td>0.043123</td>\n",
              "      <td>0.036921</td>\n",
              "      <td>0.032339</td>\n",
              "      <td>0.021660</td>\n",
              "      <td>0.049590</td>\n",
              "      <td>-0.732561</td>\n",
              "      <td>...</td>\n",
              "      <td>0.042248</td>\n",
              "      <td>0.027942</td>\n",
              "      <td>0.072569</td>\n",
              "      <td>0.026920</td>\n",
              "      <td>0.062272</td>\n",
              "      <td>0.022780</td>\n",
              "      <td>0.038004</td>\n",
              "      <td>0.026454</td>\n",
              "      <td>0.082085</td>\n",
              "      <td>0.056733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>420</td>\n",
              "      <td>0.060517</td>\n",
              "      <td>0.063444</td>\n",
              "      <td>0.049392</td>\n",
              "      <td>0.053473</td>\n",
              "      <td>0.044197</td>\n",
              "      <td>0.041027</td>\n",
              "      <td>0.028394</td>\n",
              "      <td>0.056959</td>\n",
              "      <td>-0.750094</td>\n",
              "      <td>...</td>\n",
              "      <td>0.048415</td>\n",
              "      <td>0.033778</td>\n",
              "      <td>0.083678</td>\n",
              "      <td>0.033630</td>\n",
              "      <td>0.071367</td>\n",
              "      <td>0.028768</td>\n",
              "      <td>0.043587</td>\n",
              "      <td>0.032701</td>\n",
              "      <td>0.095549</td>\n",
              "      <td>0.069169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>430</td>\n",
              "      <td>0.053286</td>\n",
              "      <td>0.070526</td>\n",
              "      <td>0.039808</td>\n",
              "      <td>0.061577</td>\n",
              "      <td>0.040209</td>\n",
              "      <td>0.046623</td>\n",
              "      <td>0.025096</td>\n",
              "      <td>0.036639</td>\n",
              "      <td>-0.611048</td>\n",
              "      <td>...</td>\n",
              "      <td>0.049428</td>\n",
              "      <td>0.037921</td>\n",
              "      <td>0.085859</td>\n",
              "      <td>0.038542</td>\n",
              "      <td>0.071130</td>\n",
              "      <td>0.033154</td>\n",
              "      <td>0.042851</td>\n",
              "      <td>0.035991</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.077181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>440</td>\n",
              "      <td>0.036665</td>\n",
              "      <td>0.061641</td>\n",
              "      <td>0.024979</td>\n",
              "      <td>0.055165</td>\n",
              "      <td>0.030056</td>\n",
              "      <td>0.040384</td>\n",
              "      <td>0.017363</td>\n",
              "      <td>0.014928</td>\n",
              "      <td>-0.424764</td>\n",
              "      <td>...</td>\n",
              "      <td>0.045688</td>\n",
              "      <td>0.035198</td>\n",
              "      <td>0.079430</td>\n",
              "      <td>0.034602</td>\n",
              "      <td>0.065149</td>\n",
              "      <td>0.029286</td>\n",
              "      <td>0.039552</td>\n",
              "      <td>0.033156</td>\n",
              "      <td>0.089842</td>\n",
              "      <td>0.069167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa4434bd-dcfc-459d-a24a-a22230c7973c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa4434bd-dcfc-459d-a24a-a22230c7973c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''we want to export the data to excel file'''\n",
        "derivative_ZachA_LynnW.to_excel(\"derivative_ZachA_LynnW.xlsx\")\n"
      ],
      "metadata": {
        "id": "W8meMxjibmdg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"We want to plot the derivative spectra for LynnW and ZachA farms\n",
        "\"\"\"\n",
        "derivative_ZachA_LynnW.plot(x=\"Wavelength\", y=['ZachA_15_Weeds_derivative',\n",
        "                                       'ZachA_15_Soil_derivative',\n",
        "                                       'ZachA_14_Weeds_derivative',\n",
        "                                       'ZachA_14_Soil_derivative',\n",
        "                                       'ZachA_13_Weeds_derivative',\n",
        "                                       'ZachA_13_Soil_derivative',\n",
        "                                       'ZachA_12_Wheat_derivative ',\n",
        "                                       'ZachA_12_Road_derivative',\n",
        "                                       'ZachA_11_Wheat_derivative',\n",
        "                                       'ZachA_11_Soil_derivative',\n",
        "                                       'LynnW_05_CornRes_derivative',\n",
        "                                       'LynnW_05_Soil_derivative',\n",
        "                                       'LynnW_04_CornRes_derivative ',\n",
        "                                       'LynnW_04_Soil_derivative ',\n",
        "                                       'LynnW_03_CornRes_derivative ',\n",
        "                                       'LynnW_03_Soil_derivative',\n",
        "                                       'LynnW_02_CornRes_derivative ',\n",
        "                                       'LynnW_02_Soil_derivative',\n",
        "                                       'LynnW_01_CornRes_derivative ',\n",
        "                                       'LynnW_01_Soil_derivative'\n",
        "\n",
        "\n",
        "\n",
        "])\n",
        "plt.gca().get_legend().remove()\n",
        "plt.title(\"Derivative Spectra for ZachA and LynnW Farms\")\n",
        "plt.ylabel(\"Derivative Relectance\")\n",
        "plt.xlabel(\"Wavelength (nm)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "-VMQSG6aThsS",
        "outputId": "e578aaf8-9151-494c-ef65-976c94d69642"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Wavelength (nm)')"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "CODE ENDS HERE\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "tFBc2G7lao_g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8194da0a-ba73-4194-ed44-8cafa4698a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nCODE ENDS HERE\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fig,axs = plt.subplots(2,2, figsize =(10,10))\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_15_Weeds_derivative'])\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_14_Weeds_derivative'])\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_13_Weeds_derivative'])\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_12_Wheat_derivative'])\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_11_Wheat_derivative'])\n",
        "# axs[0,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_12_Road_derivative'])\n",
        "# axs[0,0].set_title(\"Zach A Farms - Weeds: April 26, 2023\")\n",
        "# axs[0,0].set_xlabel('Wavelength')\n",
        "# axs[0,0].set_ylabel('Derivative Reflectance')\n",
        "\n",
        "# axs[0,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_15_Soil_derivative'])\n",
        "# axs[0,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_15_Soil_derivative'])\n",
        "# axs[0,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_15_Soil_derivative'])\n",
        "# axs[0,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['ZachA_15_Soil_derivative'])\n",
        "# axs[0,1].set_title(\"Zach A Farms - Bare Soil: April 26, 2023\")\n",
        "# axs[0,1].set_xlabel('Wavelength')\n",
        "# axs[0,1].set_ylabel('Derivative Reflectance')\n",
        "\n",
        "# axs[1,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_05_Soil_derivative'])\n",
        "# axs[1,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_03_Soil_derivative'])\n",
        "# axs[1,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_02_Soil_derivative'])\n",
        "# axs[1,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_01_Soil_derivative'])\n",
        "# #axs[1,0].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_04_Soil_derivative'])\n",
        "# axs[1,0].set_title(\"Lynn W Farms - Bare Soil: April 26, 2023\")\n",
        "# axs[1,0].set_xlabel('Wavelength')\n",
        "# axs[1,0].set_ylabel('Derivative Reflectance')\n",
        "\n",
        "\n",
        "# axs[1,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_05_CornRes_derivative'])\n",
        "# axs[1,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_04_CornRes_derivative'])\n",
        "# axs[1,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_03_CornRes_derivative'])\n",
        "# axs[1,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_02_CornRes_derivative'])\n",
        "# axs[1,1].plot(derivative_ZachA_LynnW['Wavelength'], derivative_ZachA_LynnW['LynnW_03_CornRes_derivative'])\n",
        "# axs[1,1].set_title(\"Lynn W Farms - Corn Residue: April 26, 2023\")\n",
        "# axs[1,1].set_xlabel('Wavelength')\n",
        "# axs[1,1].set_ylabel('Derivative Reflectance')\n",
        "\n"
      ],
      "metadata": {
        "id": "HAzw39Gtf5Kd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3fp-v2esVNwB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''We want to interpolate the 1nm data at 10nm\n",
        "'''\n",
        "#We first convert the wavelength, and 1nm mean reflectance to numpy arrays\n",
        "sk_1_grass_wavelength_np = np.array(sk_1_grass[\"Wavelength\"])\n",
        "sk_1_grass_mean_np = np.array(sk_1_grass[\"Mean\"])\n",
        "\n",
        "#we create the intervals of 10nm, starting from 350nm\n",
        "xvals = np.array(range(350, 2501, 10))\n",
        "\n",
        "#we use the numpy.interp to do the interpolation for (xval,x,y)\n",
        "y_interp = np.interp(xvals,sk_1_grass_wavelength_np,sk_1_grass_mean_np)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jGDbxhnX_q5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w-_Z5G8eam5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Now we want to look at the interpolated data at 10nm\n",
        "ten_nm_interpolated_data = pd.DataFrame({'ten_nm_wavelength':xvals,'ten_nm_reflectance': y_interp} )\n",
        "ten_nm_interpolated_data.head()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "z9ZAL9FbCO6k",
        "outputId": "76896652-ed23-424a-b197-863d9550596a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ten_nm_wavelength  ten_nm_reflectance\n",
              "0                350            0.971833\n",
              "1                360            0.899792\n",
              "2                370            0.864708\n",
              "3                380            0.871583\n",
              "4                390            0.882175"
            ],
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              "      <th>ten_nm_wavelength</th>\n",
              "      <th>ten_nm_reflectance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>350</td>\n",
              "      <td>0.971833</td>\n",
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              "      <th>1</th>\n",
              "      <td>360</td>\n",
              "      <td>0.899792</td>\n",
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              "      <th>2</th>\n",
              "      <td>370</td>\n",
              "      <td>0.864708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>380</td>\n",
              "      <td>0.871583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>390</td>\n",
              "      <td>0.882175</td>\n",
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            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''we plot the interpolated results against the created 10nm intervals\n",
        "'''\n",
        "plt.plot(xvals,y_interp)\n",
        "\n",
        "plt.title(\"10nm Interpolated Reflectance\")\n",
        "plt.xlabel(\"Wavelength\")\n",
        "plt.ylabel(\"Reflectance\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "qwXK8YgiGNu6",
        "outputId": "31120573-99aa-45d6-fd3e-19e97224e4c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "''' we want to claculate the derivative using the gradient function\n",
        "dydx = np.gradient(y,x)\n",
        "'''\n",
        "derivative = np.gradient(y_interp, xvals)\n",
        "plt.plot(xvals,derivative)\n",
        "plt.title(\"Derivative Spectra on the 10nm Centres\")\n",
        "plt.xlabel(\"Wavelength\")\n",
        "plt.ylabel(\"Derivative Reflectance\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "9eQHhuMoXwAa",
        "outputId": "bee5225b-d47f-41c6-f7d4-3c6b1b6d299c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Derivative Reflectance')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "mkBvEsPGYpBd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "''' We want to generate summary plot for the 1 and 10 nm,\n",
        " and the derivative spectra\n",
        "\n",
        "'''\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax1.plot(xvals, y_interp, 'ro')\n",
        "ax1.plot(sk_1_grass.Wavelength, sk_1_grass.Mean, 'b-')\n",
        "ax2.plot(xvals,derivative, \"-\")\n",
        "\n",
        "ax1.set_xlabel(\"Wavelength\")\n",
        "ax1.set_ylabel(\"Reflectance\")\n",
        "ax2.set_ylabel(\"Derivative Spectra\")\n",
        "plt.title(\"Summary Spectral Plots\")\n",
        "ax1.legend(labels = ['10nm Reflectance','1nm Reflectance', 'Derivative Spectra'])\n",
        "ax2.legend(labels = [\"Derivative Spectra\"], loc=\"upper left\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# plt.plot(xvals,y_interp, \"*\")\n",
        "# plt.plot(sk_1_grass.Wavelength, sk_1_grass.Mean, \"-\")\n",
        "# plt.plot(xvals,derivative, \"--\")\n",
        "# plt.title(\"Summary Spectral Plots\")\n",
        "# plt.xlabel(\"Wavelength\")\n",
        "# plt.ylabel(\"Reflectance\")\n",
        "# plt.legend(labels = ['10nm Reflectance','1nm Reflectance','Derivative Spectra'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "g8kNhT6LI5ih",
        "outputId": "6e05c72e-e529-4b02-aef7-85c6cc7b2734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ten_nm_interpolated_data['derivative'] = derivative\n",
        "ten_nm_interpolated_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "S20nW-KjqhCX",
        "outputId": "0c63ccde-9b24-48fc-e93c-eabe97fce6a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ten_nm_wavelength  ten_nm_reflectance  derivative\n",
              "0                  350            0.971833   -0.007204\n",
              "1                  360            0.899792   -0.005356\n",
              "2                  370            0.864708   -0.001410\n",
              "3                  380            0.871583    0.000873\n",
              "4                  390            0.882175    0.003025\n",
              "..                 ...                 ...         ...\n",
              "211               2460            1.680669   -0.012969\n",
              "212               2470            1.302918   -0.016027\n",
              "213               2480            1.360125    0.002860\n",
              "214               2490            1.360125    0.000000\n",
              "215               2500            1.360125    0.000000\n",
              "\n",
              "[216 rows x 3 columns]"
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              "          document.querySelector('#df-3a513828-d227-4d2d-8358-2dba7283b0e6 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a513828-d227-4d2d-8358-2dba7283b0e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''we want to export the data to excel file'''\n",
        "ten_nm_interpolated_data.to_excel(\"ten_nm_interpolated_data.xlsx\")\n",
        "print(ten_nm_interpolated_data)\n"
      ],
      "metadata": {
        "id": "tp9K6vTItU6p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "903afef4-7395-43b2-a698-e7d9ebba1f47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     ten_nm_wavelength  ten_nm_reflectance  derivative\n",
            "0                  350            0.971833   -0.007204\n",
            "1                  360            0.899792   -0.005356\n",
            "2                  370            0.864708   -0.001410\n",
            "3                  380            0.871583    0.000873\n",
            "4                  390            0.882175    0.003025\n",
            "..                 ...                 ...         ...\n",
            "211               2460            1.680669   -0.012969\n",
            "212               2470            1.302918   -0.016027\n",
            "213               2480            1.360125    0.002860\n",
            "214               2490            1.360125    0.000000\n",
            "215               2500            1.360125    0.000000\n",
            "\n",
            "[216 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ten_nm_interpolated_data.drop('ten_nm_reflectance', axis = 1).corr(method='pearson', min_periods=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "6gvcmIN7qk2V",
        "outputId": "b43d37a9-e29a-4166-8ae4-8e116808e224"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ten_nm_wavelength  derivative\n",
              "ten_nm_wavelength           1.000000   -0.199444\n",
              "derivative                 -0.199444    1.000000"
            ],
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              "      <th></th>\n",
              "      <th>ten_nm_wavelength</th>\n",
              "      <th>derivative</th>\n",
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              "  </thead>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.199444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>derivative</th>\n",
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              "          const element = document.querySelector('#df-ef9cecf5-cdc8-4677-b7ad-4cad6e916d57');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YMu-4GUxqlDf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zsIjgJJuqlNa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" CODE ENDS HERE\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bSFB7EZuXtcM",
        "outputId": "a0eaa91a-4c83-46b7-daa5-73a492e72986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' CODE ENDS HERE'"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 160
        }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SPzjrRdN_QXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#'''we want to calculate the moving average of the reflectance'''\n",
        "\n",
        "# we calculate the simple moving average, using window size of 10nm\n",
        "\n",
        "#sk_1_grass['moving_average'] = sk_1_grass['Mean'].rolling(10).mean()\n",
        "\n",
        "# we want to drop the NA values for the first nine rows after calculating the moving average\n",
        "#sk_1_grass['moving_average'].dropna(inplace=True)\n",
        "\n",
        "# we make sure the columns are indicated in the table\n",
        "#sk_1_grass.columns"
      ],
      "metadata": {
        "id": "zAfUzXOG8Ctd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#sk_1_grass.moving_average[0:30] # we are just looking at the moving average"
      ],
      "metadata": {
        "id": "B_RppB2jZTgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''What we want to do is to match the 10nm dataframe created above with the moving\n",
        "average reflectance. The first moving average starts from the 10th row (row 9).\n",
        "We want to select the moving averages in intervals of 10 and macth\n",
        "them with the 10nm wavelengths with centre\n",
        "starting from 355nm.'''\n",
        "\n",
        "#We slice the moving averages 10nm intervals\n",
        "\n",
        "\n",
        "# len(sk_1_grass.moving_average[9:1922:10])\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hX-aISInVA4X",
        "outputId": "df1398be-0d5d-4e05-c1b5-5f74f4f7578f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#What we want to do next is to match the centre of the moving averages to the centre of the 10nm bands\n",
        "#we start from 355 to 365, etc. in 10nm intervals\n",
        "# ten_nm = [i for i in range(355,2275,10)]\n",
        "# len(ten_nm)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jsBG-j7sVjAp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "608e6cfa-d581-432a-ac54-89d2254f4da7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we convert the list  of 10nm centres to dataframe\n",
        "\n",
        "# W_ten_nm = pd.DataFrame({\"ten_nm\": ten_nm, \"centered_mov_av\":sk_1_grass.moving_average[9:1922:10]} )\n",
        "# W_ten_nm\n"
      ],
      "metadata": {
        "id": "o_igZk8Ud4HI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''We want to\n",
        "    plot the moving averages against the 10nm centers\n",
        "'''\n",
        "# plt.plot(W_ten_nm.ten_nm, W_ten_nm.centered_mov_av)\n",
        "# plt.title(\"10nm Moving Average Reflectance\")\n",
        "# plt.xlabel(\"Wavelength\")\n",
        "# plt.ylabel(\"Reflectance\")"
      ],
      "metadata": {
        "id": "A22vrz9Qkgtc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c32a7a11-8f36-44ad-db55-2c503c01fd8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'We want to\\n    plot the moving averages against the 10nm centers\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "''' We want to perform the first derivative of the 10 nm spectra drdl\n",
        "\n",
        "             '''\n",
        "# wavelegth_diff = W_ten_nm.ten_nm.diff(periods=2)\n",
        "# reflectance_diff = W_ten_nm.centered_mov_av.diff(periods=2)\n",
        "# derivative = reflectance_diff/wavelegth_diff\n",
        "# derivative"
      ],
      "metadata": {
        "id": "XkTirqvienIi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "678b7dd3-95c5-4472-910c-d256af058004"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' We want to perform the first derivative of the 10 nm spectra drdl\\n\\n             '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "frnCmrFYzBVG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\" we create a new 10nm list starting from 365nm\"\"\"\n",
        "'''we drop the first endpoint (350nm) and move\n",
        "the 10nm wavlength one row up to match the centers ,\n",
        "                             '''\n",
        "# new_ten_nm = [i for i in range(365,2285,10)]\n",
        "# len(new_ten_nm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "r3B2RnXaA-xO",
        "outputId": "ba722af4-94aa-4837-c8ed-f33bb7a17bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'we drop the first endpoint (350nm) and move\\nthe 10nm wavlength one row up to match the centers ,\\n                             '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# len (derivative)\n"
      ],
      "metadata": {
        "id": "J16sYMaZ7drb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# derivative_reflectance = pd.DataFrame({\"new_ten_nm\": new_ten_nm, \"derivative\": derivative})\n",
        "# derivative_reflectance"
      ],
      "metadata": {
        "id": "s4CSN0xTCiub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''We want to\n",
        "    plot the derivative spectra against the 10nm centers\n",
        "    starting from 365nm.\n",
        "    What we did no do here is that we eliminated the first end point (350nm)\n",
        "    We also kept the last endpoint to 20nm difference but not 10nm\n",
        "'''\n",
        "# plt.plot(derivative_reflectance.new_ten_nm, derivative_reflectance.derivative)\n",
        "# plt.title(\"10nm derivative spectra\")\n",
        "# plt.xlabel(\"Center Wavelength\")\n",
        "# plt.ylabel(\"Derivative Reflectace\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "iwV-Db89D0nU",
        "outputId": "c22b1a0e-834b-48a7-8456-529dad14b688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'We want to\\n    plot the derivative spectra against the 10nm centers\\n    starting from 365nm.\\n    What we did no do here is that we eliminated the first end point (350nm)\\n    We also kept the last endpoint to 20nm difference but not 10nm\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0rtpAXf-FUWK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}