k_means_Clustering_of_VPCA_Spectra.ipynb

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        "K-Means Model\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "\n",
        "***The code constructs K_means clustering algorithm to processes the field reflectance data.The model partions the varimax rotated principal components of a field data into its original land cover type***\n",
        "\n",
        "Credits: Mohammed Braimah, September 2023\n"
      ]
    },
    {
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          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "\n",
        "##we are importing operational packages\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "##we are importing visualization packages\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Important tools for modeling and evaluation.\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uOjwcyUFQSW"
      },
      "source": [
        "***We want to load the excel book and call each worksheet.\n",
        "This saves us time from saving each worksheet seperately as a .csv file***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "0pvHct8eXwPl"
      },
      "outputs": [],
      "source": [
        "\n",
        "VPCA = pd.ExcelFile('/content/gdrive/MyDrive/Field_Reflectance_Preprocessed/spring_2023_vpca_components.xlsx')\n",
        "\n",
        "component = pd.read_excel(VPCA, 'Sheet1')\n",
        "components = component.drop([59])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "components"
      ],
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        "id": "zSKGELIZhSUS",
        "outputId": "f48c9c21-079b-4cd8-e105-cc66f79d4d99"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "0       KB_5_Corn_Res_derivative        0.58711        1.45673       -0.78512   \n",
              "1       KB_3_Corn_Res_derivative        0.47937        1.42302       -0.83547   \n",
              "2       KB_1_Corn_Res_derivative        0.51947        1.47856       -0.84628   \n",
              "3       JB_5_Corn_Res_derivative        0.22135        0.59156       -0.27654   \n",
              "4       JB_4_Corn_Res_derivative       -0.36003       -0.36478       -1.54492   \n",
              "..                           ...            ...            ...            ...   \n",
              "56     JanD_S10_Weeds_derivative       -0.16037       -0.77824       -0.10602   \n",
              "57      JanD_S9_Weeds_derivative       -0.20803       -1.18310        0.10094   \n",
              "58     ZachA_12_Wheat_derivative       -0.18920       -1.28749       -0.23878   \n",
              "60         KB_5_Wheat_derivative       -0.26773       -1.34868       -0.64563   \n",
              "61  KB_5_Drying_Wheat_derivative       -0.14781       -0.75104       -0.69937   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  \n",
              "0         0.95528        0.85001        1.35848  \n",
              "1         1.16594        0.95517        0.77655  \n",
              "2         1.32242        1.13929        0.74541  \n",
              "3        -0.63017        0.09259        0.34682  \n",
              "4        -1.79321       -1.13306        0.53164  \n",
              "..            ...            ...            ...  \n",
              "56        0.06051        0.77434        0.44745  \n",
              "57        0.00556        1.22714        0.34596  \n",
              "58        0.01147        0.45827        0.27256  \n",
              "60       -0.80697        0.61665        0.38074  \n",
              "61        0.26585       -2.86606        0.51895  \n",
              "\n",
              "[61 rows x 7 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KB_5_Corn_Res_derivative</td>\n",
              "      <td>0.58711</td>\n",
              "      <td>1.45673</td>\n",
              "      <td>-0.78512</td>\n",
              "      <td>0.95528</td>\n",
              "      <td>0.85001</td>\n",
              "      <td>1.35848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KB_3_Corn_Res_derivative</td>\n",
              "      <td>0.47937</td>\n",
              "      <td>1.42302</td>\n",
              "      <td>-0.83547</td>\n",
              "      <td>1.16594</td>\n",
              "      <td>0.95517</td>\n",
              "      <td>0.77655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KB_1_Corn_Res_derivative</td>\n",
              "      <td>0.51947</td>\n",
              "      <td>1.47856</td>\n",
              "      <td>-0.84628</td>\n",
              "      <td>1.32242</td>\n",
              "      <td>1.13929</td>\n",
              "      <td>0.74541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JB_5_Corn_Res_derivative</td>\n",
              "      <td>0.22135</td>\n",
              "      <td>0.59156</td>\n",
              "      <td>-0.27654</td>\n",
              "      <td>-0.63017</td>\n",
              "      <td>0.09259</td>\n",
              "      <td>0.34682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JB_4_Corn_Res_derivative</td>\n",
              "      <td>-0.36003</td>\n",
              "      <td>-0.36478</td>\n",
              "      <td>-1.54492</td>\n",
              "      <td>-1.79321</td>\n",
              "      <td>-1.13306</td>\n",
              "      <td>0.53164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>JanD_S10_Weeds_derivative</td>\n",
              "      <td>-0.16037</td>\n",
              "      <td>-0.77824</td>\n",
              "      <td>-0.10602</td>\n",
              "      <td>0.06051</td>\n",
              "      <td>0.77434</td>\n",
              "      <td>0.44745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>JanD_S9_Weeds_derivative</td>\n",
              "      <td>-0.20803</td>\n",
              "      <td>-1.18310</td>\n",
              "      <td>0.10094</td>\n",
              "      <td>0.00556</td>\n",
              "      <td>1.22714</td>\n",
              "      <td>0.34596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>ZachA_12_Wheat_derivative</td>\n",
              "      <td>-0.18920</td>\n",
              "      <td>-1.28749</td>\n",
              "      <td>-0.23878</td>\n",
              "      <td>0.01147</td>\n",
              "      <td>0.45827</td>\n",
              "      <td>0.27256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>KB_5_Wheat_derivative</td>\n",
              "      <td>-0.26773</td>\n",
              "      <td>-1.34868</td>\n",
              "      <td>-0.64563</td>\n",
              "      <td>-0.80697</td>\n",
              "      <td>0.61665</td>\n",
              "      <td>0.38074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>KB_5_Drying_Wheat_derivative</td>\n",
              "      <td>-0.14781</td>\n",
              "      <td>-0.75104</td>\n",
              "      <td>-0.69937</td>\n",
              "      <td>0.26585</td>\n",
              "      <td>-2.86606</td>\n",
              "      <td>0.51895</td>\n",
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          },
          "metadata": {},
          "execution_count": 81
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    },
    {
      "cell_type": "markdown",
      "source": [
        "***We are begining the machine learning by cluster centres using K means***"
      ],
      "metadata": {
        "id": "Dxsq4f9xI-78"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We are assigning the visible spectrum to a variable called X. Remember we have set the wavelength column as index.\n",
        "# The transposed matrix has wavelengths as column headings\n",
        "X = components.drop('Sample', axis =1 )"
      ],
      "metadata": {
        "id": "82wTVpqyE6-8"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We want to transform the data and save it to variable `X_scaled`.\n",
        "\n",
        "X_scaled = StandardScaler().fit_transform(X)"
      ],
      "metadata": {
        "id": "INt-knl929yg"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***This Function is used to find an optimized cluster Centres. The Inertia which measures the squared distances of the intra-cluster distances are applied***"
      ],
      "metadata": {
        "id": "ErMt9_cgJ9-h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We fit K-means and evaluate inertia for different values of k.\n",
        "num_clusters = [i for i in range(2, 12)]\n",
        "\n",
        "def kmeans_inertia(num_clusters, x_vals):\n",
        "    \"\"\"\n",
        "    Accepts as arguments list of ints and data array.\n",
        "    Fits a KMeans model where k = each value in the list of ints.\n",
        "    Returns each k-value's inertia appended to a list.\n",
        "    \"\"\"\n",
        "    inertia = []\n",
        "    for num in num_clusters:\n",
        "        kms = KMeans(n_clusters=num, max_iter = 1000, random_state=42)\n",
        "        kms.fit(x_vals)\n",
        "        inertia.append(kms.inertia_)\n",
        "\n",
        "    return inertia\n",
        "\n",
        "#model = KMeans.fit(X_scaled, k=2, random_state = 42)"
      ],
      "metadata": {
        "id": "Z094sgg_FWPP"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We return a list of inertia for k=2 to 10.\n",
        "\n",
        "inertia = kmeans_inertia(num_clusters, X_scaled)\n",
        "inertia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gW2yEiuhFiC4",
        "outputId": "ed3a0709-c74f-43d7-d199-765302782134"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[282.56739689383534,\n",
              " 231.9231204594956,\n",
              " 189.22205533424017,\n",
              " 156.13215989612013,\n",
              " 136.85092599544782,\n",
              " 116.65262927308683,\n",
              " 104.00243085334722,\n",
              " 96.8296246035528,\n",
              " 86.4897672560746,\n",
              " 78.60666639776846]"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We want to plot the intertia curve\n",
        "plot = sns.lineplot(x=num_clusters, y=inertia)\n",
        "plot.set_xlabel(\"Number of clusters\");\n",
        "plot.set_ylabel(\"Inertia\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Oy8zI6B1Fw8r",
        "outputId": "3fbcb55d-7989-4b55-8c5b-3fe14725a52d"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RWFiocxxBEO5Y1q9f39SnYzJsLM2xeGRnmEsEbE/JxcakS2KXREREdFeihp24uDiEh4fj4MGDiI2NRUVFBQYMGICSkhIAQHZ2NrKzs/Hpp58iJSUFa9aswY4dOzBx4sQ7jrV69Wrk5ORol7CwsCY+G9PSyctBe3fl97ecxMVrJSJXREREVDNBo0fXIK5evQpXV1fExcWhd+/eNe6zceNGjB49GiUlJTA3NwdQPbITExNT74CjVCohl8tRWFgIe3v7+pZvcqrUGoz630EcvJCPQC85/m9SCCzMeGWUiIiaRm2/v/Xqm+n25SmFQnHPfezt7bVB57bw8HA4OzujR48eWLVq1T37SFQqFZRKpc5CdWcmEbBoRGfYW5nj2KVCLNl5TuySiIiI7qA3YUetVmPKlCkIDQ2Fv79/jftcu3YNH374IV555RWd9fPmzcPPP/+M2NhYDB8+HK+//jq++OKLu35WVFQU5HK5dvH29m7QczElng7WiLo1HX3Z3vNIuHBd5IqIiIh06c1lrEmTJmH79u3Yv38/vLy87tiuVCrx2GOPQaFQYMuWLbCwsLjrsebMmYPVq1cjK6vm5zipVCqoVP88wVupVMLb25uXsR7AjI3HsDHpEjzlVtg+pTfk1nf//RARETUEg7qMFRERgW3btmHPnj01Bp2ioiIMGjQIMpkMMTEx9ww6ANCzZ09cunRJJ9D8m1Qqhb29vc5CD2buEx3R3MkG2YVleC+G09GJiEh/iBp2NBoNIiIiEBMTg927d8PPz++OfZRKJQYMGABLS0ts2bIFVlZW9z1ucnIyHB0dIZVKG6NsqoGd1ByLn+sMM4mAbcdzsOnIZbFLIiIiAgCY33+XxhMeHo5169bh119/hUwmQ25uLgBALpfD2tpaG3RKS0uxdu1anWZiFxcXmJmZYevWrcjLy0OvXr1gZWWF2NhYzJ8/H2+99ZaYp2aSgnwcMbV/a3z651nM+TUF3Xwd0dzJVuyyiIjIxInasyMIQo3rV69ejfHjx2Pv3r3o27dvjftcvHgRvr6+2LFjByIjI3H+/HloNBq0atUKkyZNwssvvwyJpHYDV5x63nCq1BqMXBGPxPQbCPJxwMZXg2HO6ehERNQIavv9rTcNymJi2GlYl26UYvCSv1BUVok3+7XGtMfaiF0SEREZIYNqUCbj4uVog4+fCgAAfLn7HA6n54tcERERmTKGHWoUTwR64umgZlBrgCkbkqEsqxC7JCIiMlEMO9RoPniyI7wV1rh04ybmbE4RuxwiIjJRDDvUaGRWFlj8XBDMJAI2J2dj81FORycioqbHsEONqmtzR7zxaCsAwOzNKcjKLxW5IiIiMjUMO9ToIvq2QtfmjihSVWLqhmRUVqnFLomIiEwIww41OnMzCRY/1xl2UnMczriBr/amiV0SERGZEIYdahLeCht8GNYRALBk1zkcybwhckVERGQqGHaoyYR1boYnAj1RpdZgyvpkFHE6OhERNQGGHWoygiDgwzB/NHOwRmZ+Kd7fckrskoiIyAQw7FCTkltb4PPnOkMiAL8cuYStx7LFLomIiIwcww41uR5+CoT3rZ6O/m7MCVwuuClyRUREZMwYdkgUb/Zrjc7eDigqq56OXqU2+efREhFRI2HYIVFYmEmwZGRn2Fqa4dDFfCyP43R0IiJqHAw7JJrmTrZ4/4nq6eifx57FsawCcQsiIiKjxLBDonqmqxeGdPJApVqDyeuPokRVKXZJRERkZBh2SFSCIGB+WAA85FZIv16KeVs5HZ2IiBoWww6JTm5TPR1dEIANh7Ow/USO2CUREZERYdghvdCrhRMm9WkJAJi56QRyCjkdnYiIGgbDDumNKf3boJOXHIU3KzBtwzGoOR2diIgaAMMO6Q1L8+qno1tbmCH+wnWs+OuC2CUREZERYNghvdLCxQ7vP9EBAPDZn6k4calQ5IqIiMjQMeyQ3hnRzRuDOrqjokqDyRuOorSc09GJiKj+GHZI7wiCgKinA+BmL8WFqyX4cNtpsUsiIiIDxrBDesnR1hKLRlRPR//pUCb+OJkrdklERGSgGHZIb4W2csYrD7cAAMz85TjylGUiV0RERIaIYYf02rQBbdDR0x43Sivw1kZORyciorpj2CG9JjU3w5KRQbCykOCvc9ew6u+LYpdEREQGhmGH9F4rVzvMHlo9HX3hjlSczOZ0dCIiqj2GHTIIL/TwQf/2biivUmPy+mTcLK8SuyQiIjIQooadqKgodO/eHTKZDK6urggLC0NqaqrOPmVlZQgPD4eTkxPs7OwwfPhw5OXl6eyTmZmJIUOGwMbGBq6urpgxYwYqK3lvFmMiCAIWDA+Ai0yK81eKMf93TkcnIqLaETXsxMXFITw8HAcPHkRsbCwqKiowYMAAlJSUaPeZOnUqtm7dio0bNyIuLg7Z2dl4+umntdurqqowZMgQlJeX48CBA/juu++wZs0azJkzR4xTokbkZCfFZ88GAgB+OJiBXafz7vMOIiIiQNBoNHozveXq1atwdXVFXFwcevfujcLCQri4uGDdunV45plnAABnzpxB+/btER8fj169emH79u0YOnQosrOz4ebmBgBYvnw53nnnHVy9ehWWlpb3/VylUgm5XI7CwkLY29s36jnSg/tw2yms3H8RTraW2D7lYbjKrMQuiYiIRFDb72+96tkpLKxuPFUoFACApKQkVFRUoH///tp92rVrBx8fH8THxwMA4uPjERAQoA06ADBw4EAolUqcPHmyxs9RqVRQKpU6CxmOGQPbop27DNdLyjFj43FORycionvSm7CjVqsxZcoUhIaGwt/fHwCQm5sLS0tLODg46Ozr5uaG3Nxc7T7/Djq3t9/eVpOoqCjI5XLt4u3t3cBnQ43JysIMS58PgtRcgrizV/FdfLrYJRERkR7Tm7ATHh6OlJQUrF+/vtE/KzIyEoWFhdolKyur0T+TGlYbNxneG9IeABC1/QzO5HJ0joiIaqYXYSciIgLbtm3Dnj174OXlpV3v7u6O8vJyFBQU6Oyfl5cHd3d37T7/nZ11+/Xtff5LKpXC3t5eZyHDM6ZXczzazhXllWpM/ikZZRWcjk5ERHcSNexoNBpEREQgJiYGu3fvhp+fn872rl27wsLCArt27dKuS01NRWZmJoKDgwEAwcHBOHHiBK5cuaLdJzY2Fvb29ujQoUPTnAiJQhAELHymE5ztLJGaV4To7WfELomIiPSQqGEnPDwca9euxbp16yCTyZCbm4vc3FzcvHkTACCXyzFx4kRMmzYNe/bsQVJSEiZMmIDg4GD06tULADBgwAB06NABY8aMwbFjx/DHH39g1qxZCA8Ph1QqFfP0qAk420nxya3p6GsOpGNP6pX7vIOIiEyNqFPPBUGocf3q1asxfvx4ANU3FZw+fTp++uknqFQqDBw4EF999ZXOJaqMjAxMmjQJe/fuha2tLcaNG4fo6GiYm5vXqg5OPTd87285iTUH0uFsJ8WOKQ/D2Y5Bl4jI2NX2+1uv7rMjFoYdw1dWUYUnv/wbqXlFeLSdK1aO63bXME1ERMbBIO+zQ1RfVhZmWPJ8Z1iaS7D7zBWsPZghdklERKQnGHbIaLRzt8fMQe0AAB/9dhpn84pEroiIiPQBww4ZlQmhvujTxgWqSjXe/OkoVJWcjk5EZOoYdsioCIKAT57tBIWtJc7kFuGTHalil0RERCJj2CGj4yqzwifPdAIA/G//Rew7e1XkioiISEwMO2SU+rV3w5hezQEA0zceQ35JucgVERGRWBh2yGi9+3h7tHK1w9UiFd755Th4lwUiItNUu7vu1eDw4cP4+eefkZmZifJy3X81b9q06YELI3pQ1pZmWDKyM55adgCxp/Kw7lAmRvVsLnZZRETUxOo1srN+/XqEhITg9OnTiImJQUVFBU6ePIndu3dDLpc3dI1E9dbRU463B7UFAHy47RTOXykWuSIiImpq9Qo78+fPx+eff46tW7fC0tISS5YswZkzZzBixAj4+Pg0dI1ED+TFUD881MoZZRVqTF5/FOWVarFLIiKiJlSvsJOWloYhQ4YAACwtLVFSUgJBEDB16lSsWLGiQQskelASiYDPRgTC0cYCJ7OVmPnLcajV7N8hIjIV9Qo7jo6OKCqqvjtts2bNkJKSAgAoKChAaWlpw1VH1EDc7K2w6LnOMJMI2HT0Mj7+/TQblomITES9wk7v3r0RGxsLAHj22WcxefJkvPzyy3j++efRr1+/Bi2QqKH0beuKhcOr77+zcv9FfLU3TeSKiIioKdRrNtaXX36JsrIyAMB7770HCwsLHDhwAMOHD8esWbMatECihjS8qxdulJbjo99O45M/UqGwtcTzPdhnRkRkzAQNx/Jr/Yh4Mh4Ld5zBV3vTIBGAZS90weAAD7FLIiKiOqrt93etR3aUSqX2QEql8p77MjCQvpsxsC1ulJbjp0NZmLw+GXJrC4S0cha7LCIiagS17tlxdHTElStXAAAODg5wdHS8Y7m9nkjfCYKAj8ICMKijO8qr1Hj5+8M4fqlA7LKIiKgR1HpkZ/fu3VAoFACAPXv2NFpBRE3FTCJgyfOdMWF1Ig6kXcf41YnY+FowWrrYiV0aERE1oHr17GRmZsLb2xuCIOis12g0yMrKMrgbC7Jnx7QVqyrx/IqDOHG5EJ5yK/zyegg85NZil0VERPdR2+/vek099/Pzw9WrV+9Yn5+fDz8/v/ockkg0dlJzrJnQHS2cbZFdWIYxKw/hBp+STkRkNOoVdjQazR2jOgBQXFwMKyurBy6KqKk52Unx/cQecLe3wvkrxZiwJhElqkqxyyIiogZQp/vsTJs2DUB1c+fs2bNhY2Oj3VZVVYWEhAR07ty5QQskaipejjb4YWIPPPtNPJKzCvDa2iSsHNcdlub1+jcBERHpiTqFnaNHjwKoHtk5ceIELC0ttdssLS0RGBiIt956q2ErJGpCrd1kWDW+O0Z9m4C/zl3DtJ+TsWRkEMwkd45kEhGRYahXg/KECROwdOlSyGSyxqipybFBmf4r7uxVvPRdIiqqNBjTqznmPdmxxku3REQknkZrUK6oqMAPP/yAjIyMByqQSJ/1aeOCRSM6QxCAHw5mYPHOc2KXRERE9VTnsGNhYQEfHx9UVVU1Rj1EemNYoCfmPdERALBk1zms+fuiyBUREVF91Kvz8r333sO7776L/Pz8hq6HSK+MCfbFlP6tAQDvbz2FX5Mvi1wRERHVVb2fen7+/Hl4enqiefPmsLW11dl+5MiRBimOSB9M7tcaN0rK8V18Bqb/fAxyaws80tZV7LKIiKiW6hV2wsLCGrgMIv0lCALmDuuIG6UV2HIsG5PWHsHal3qia3M+B46IyBDUazaWseFsLKqN8ko1Xvr+MPadvQq5tQU2vhaMNm7GMSORiMgQNerjIgCgoKAA//vf/xAZGant3Tly5AguX2ZPAxknS3MJlo/ugiAfBxTerMCYlQnIyi8VuywiIrqPeoWd48ePo02bNliwYAE+/fRTFBQUAAA2bdqEyMjIWh9n3759GDZsGDw9PSEIAjZv3qyzXRCEGpdPPvlEu4+vr+8d26Ojo+tzWkT3ZWNpjtXju6ONmx3ylCqMXXUI14pVYpdFRET3UK+wM23aNIwfPx7nzp3TeRbW448/jn379tX6OCUlJQgMDMSyZctq3J6Tk6OzrFq1CoIgYPjw4Tr7zZs3T2e/N954oz6nRVQrDjaW+P7FnmjmYI2L10owbtUhFJVViF0WERHdRb0alBMTE/HNN9/csb5Zs2bIzc2t9XEGDx6MwYMH33W7u7u7zutff/0Vffv2RYsWLXTWy2SyO/a9F5VKBZXqn3+NK5XKWr+XCADc5VbVz9FaHo+T2Uq8/P1hrJnQA1YWZmKXRkRE/1GvkR2pVFpjQDh79ixcXFweuKia5OXl4bfffsPEiRPv2BYdHQ0nJycEBQXhk08+QWXlvZ9WHRUVBblcrl28vb0bpWYybi1c7LBmQg/YSc1x8EI+Jq8/isoqtdhlERHRf9Qr7DzxxBOYN28eKiqqh+4FQUBmZibeeeedOy4xNZTvvvsOMpkMTz/9tM76N998E+vXr8eePXvw6quvYv78+Xj77bfveazIyEgUFhZql6ysrEapmYxfgJccK8Z2haWZBH+czMN7MSngBEciIv1Sr6nnhYWFeOaZZ3D48GEUFRXB09MTubm5CA4Oxu+//37HTQZrVYggICYm5q738GnXrh0ee+wxfPHFF/c8zqpVq/Dqq6+iuLgYUqm0Vp/Nqef0oHak5OL1H5Og1gCv9WmJmYPbiV0SEZHRq+33d716duRyOWJjY7F//34cP34cxcXF6NKlC/r371/vgu/lr7/+QmpqKjZs2HDffXv27InKykqkp6ejbdu2jVIP0X8N8ndH1NMBeOeXE1gelwYnW0u83LvF/d9IRESNrl5h57aHHnoIDz30UEPVclcrV65E165dERgYeN99k5OTIZFI4OrK2/lT03quuw/ySyqwYMcZfPz7aTjYWODZbuwHIyISW73Dzq5du7Br1y5cuXIFarVuU+aqVatqdYzi4mKcP39e+/rixYtITk6GQqGAj48PgOohqo0bN+Kzzz674/3x8fFISEhA3759IZPJEB8fj6lTp2L06NFwdOSt/KnpvdanBfJLVPj2r4uYuekEHGws8VgHN7HLIiIyafVqUP7ggw8wYMAA7Nq1C9euXcONGzd0lto6fPgwgoKCEBQUBKD6/j1BQUGYM2eOdp/169dDo9Hg+eefv+P9UqkU69evR58+fdCxY0d8/PHHmDp1KlasWFGf0yJ6YIIg4N3H22N4Fy9UqTUIX3cECReui10WEZFJq1eDsoeHBxYuXIgxY8Y0Rk1Njg3K1NAqq9R4be0R7DydB5nUHOtf7YWOnnKxyyIiMiqN+mys8vJyhISE1Ls4ImNnbibBly8EoYefAkWqSoxbdQjp10rELouIyCTVK+y89NJLWLduXUPXQmRUrCzM8L9x3dDewx7XissxZlUCrijLxC6LiMjk1KtBuaysDCtWrMDOnTvRqVMnWFhY6GxftGhRgxRHZOjsrSzw3Yvd8ezyeGRcL8XYVYew4ZVgyG0s7v9mIiJqEPXq2enbt+89t+/Zs6feBYmBPTvU2DKvl2L48gO4WqRCt+aO+GFiT1hb8jlaREQPorbf3/UKO8aGYYeawukcJUZ8E4+iskr0beuCFWO7wcKsXleSiYgIjRR2/vtcqhoPKAj45ZdfantIvcCwQ00lMT0fY1YmoKxCjaeCmuGzZwMhkQhil0VEZJAa5XERcjmnzhI9iO6+Cnw1qgte/j4JMUcvw8HGAnOGdoAgMPAQETUWXsYCR3ao6cUcvYSpG44BAN4a0AYRj7YWuSIiIsPTqPfZIaIH81SQF2YP7QAA+PTPs/gxIUPkioiIjBfDDpFIJj7kh4i+rQAAszan4PcTOSJXRERknBh2iEQ0fUAbvNDTBxoNMGV9MvafuyZ2SURERodhh0hEgiDgwyf98XiAO8qr1Hjlh8M4llUgdllEREaFYYdIZGYSAZ8/1xmhrZxQWl6F8asP4fyVYrHLIiIyGgw7RHpAam6Gb8Z0Q6CXHDdKKzB2ZQKyC26KXRYRkVFg2CHSE3ZSc6ye0AMtXGyRXViGMSsTkF9SLnZZREQGj2GHSI8obC3xw8Se8JBbIe1qCSasSUSJqlLssoiIDBrDDpGeaeZgjR8m9oCjjQWOZRXgtbVJUFVWiV0WEZHBYtgh0kOtXGVYPaEHbCzN8Ne5a5j28zFUqU3+ZudERPXCsEOkpzp7O+CbMV1hYSbgt+M5mPNrCvh0FyKiumPYIdJjD7d2wefPdYYgAD8mZOLz2LNil0REZHAYdoj03NBOnvjwSX8AwNLd57H674siV0REZFgYdogMwOhezTHtsTYAgA+2nsLmo5dFroiIyHAw7BAZiDcebYXxIb4AgLc2HsOPCRlQs2mZiOi+GHaIDIQgCJgztAPCOnuiUq3BezEpGPntQT5agojoPhh2iAyIRCLgsxGdMWtIe1hbmOHQxXw8vuQvLNl5DuWVarHLIyLSSww7RAbGTCLgpYdb4M+pvfFIWxeUV6nx+c6zGLL0LxxOzxe7PCIivcOwQ2SgvBU2WD2+O5Y+HwRnO0ucu1KMZ5bH472YE1CWVYhdHhGR3mDYITJggiDgiUBP7JzWByO6eQGovh9P/8/isCMlhzchJCICww6RUXCwscTCZwKx7uWe8HO2xZUiFV5bewSv/JCEnMKbYpdHRCQqhh0iIxLS0hnbJz+MNx5tBXOJgNhTeXhs0T58dyCdz9YiIpMlatjZt28fhg0bBk9PTwiCgM2bN+tsHz9+PARB0FkGDRqks09+fj5GjRoFe3t7ODg4YOLEiSgu5lRcMl1WFmaYPqAtfnvzYQT5OKBYVYm5W07imeUHcCZXKXZ5RERNTtSwU1JSgsDAQCxbtuyu+wwaNAg5OTna5aefftLZPmrUKJw8eRKxsbHYtm0b9u3bh1deeaWxSyfSe23dZfjltRDMe7Ij7KTmOJpZgKFL9+PTP1JRVlEldnlERE1G0OhJB6MgCIiJiUFYWJh23fjx41FQUHDHiM9tp0+fRocOHZCYmIhu3boBAHbs2IHHH38cly5dgqenZ43vU6lUUKlU2tdKpRLe3t4oLCyEvb19g50Tkb7IKbyJOb+eROypPACAn7MtPn7KHyEtnUWujIio/pRKJeRy+X2/v/W+Z2fv3r1wdXVF27ZtMWnSJFy/fl27LT4+Hg4ODtqgAwD9+/eHRCJBQkLCXY8ZFRUFuVyuXby9vRv1HIjE5iG3xrdju2H56K5wlUlx8VoJXvg2ATM2HkNBabnY5RERNSq9DjuDBg3C999/j127dmHBggWIi4vD4MGDUVVVPQSfm5sLV1dXnfeYm5tDoVAgNzf3rseNjIxEYWGhdsnKymrU8yDSF4P83bFzeh+M7uUDANiYdAn9F8Xh1+TLnKZOREbLXOwC7mXkyJHaPwcEBKBTp05o2bIl9u7di379+tX7uFKpFFKptCFKJDI49lYW+CgsAGGdmyFy0wmcu1KMyeuTEXP0Mj580h/eChuxSyQialB6PbLzXy1atICzszPOnz8PAHB3d8eVK1d09qmsrER+fj7c3d3FKJHIYHTzVeC3Nx/GtMfawNJMgr2pVzHg8334318XUFnF52wRkfEwqLBz6dIlXL9+HR4eHgCA4OBgFBQUICkpSbvP7t27oVar0bNnT7HKJDIYluYSvNmvNbZPeRg9/BS4WVGFj347jbCv/kbK5UKxyyMiahCizsYqLi7WjtIEBQVh0aJF6Nu3LxQKBRQKBT744AMMHz4c7u7uSEtLw9tvv42ioiKcOHFCexlq8ODByMvLw/Lly1FRUYEJEyagW7duWLduXa3rqG03N5ExU6s1+PlwFub/fhrKskqYSQRMfMgPU/q3ho2lXl/xJiITVdvvb1HDzt69e9G3b9871o8bNw5ff/01wsLCcPToURQUFMDT0xMDBgzAhx9+CDc3N+2++fn5iIiIwNatWyGRSDB8+HAsXboUdnZ2ta6DYYfoH1eKyjBv6ylsO54DAPBWWOOjsAD0aeMicmVERLoMIuzoC4YdojvtPpOHWTEpyC4sAwCEdfbErKEd4GzH5n4i0g9Gc58dIhLHo+3cEDutD14M9YNEADYnZ6P/ojhsPJzFaepEZFAYdojormyl5pgzrANiXg9Few97FJRWYMb/Hceo/yUg/VqJ2OUREdUKww4R3VegtwO2RIRi5uB2kJpLcCDtOgYu3oev9p5HBaepE5GeY9gholqxMJPgtT4t8efU3niolTNUlWos3JGKYV/sx9HMG2KXR0R0Vww7RFQnzZ1s8cPEHlg0IhCONhY4k1uEp78+gPe3nESxqlLs8oiI7sCwQ0R1JggCnu7ihZ3T+uDpoGbQaIA1B9Lx2KI47Lz1ZHUiIn3BsENE9eZkJ8Wi5zrjh4k94KOwQU5hGV76/jBe/zEJV5RlYpdHRASAYYeIGsDDrV3wx5TeeK1PS5hJBPx+Ihf9FsVhXUIm1GpOUycicTHsEFGDsLY0w8zB7bAlIhSdvOQoKqvEuzEn8NyKeJy/UiR2eURkwhh2iKhBdfSUI+b1UMwe2gE2lmZITL+Bx5fsx+KdZ6GqrBK7PCIyQQw7RNTgbj9E9M+pvdG3rQvKq9RYvPMcHl/yFw5dzBe7PCIyMQw7RNRovBxtsGp8d3z5QhCc7aRIu1qCEd/EI3LTCRTerBC7PCIyEQw7RNSoBEHA0E6e2DWtD0Z29wYA/HQoE/0XxeH3Ezl8zhYRNTqGHSJqEnIbC0QP74SfXu6FFs62uFqkwus/HsGrPyThShGnqRNR42HYIaImFdzSCb9PfhhvPtoK5hIBf57Kw4DP92Hz0csc5SGiRsGwQ0RNzsrCDNMGtMWWiIfQ0bP6aepTNiTj5e95M0IiangMO0Qkmg6e9tgcHorpj7WBhZmAnafz8Njn+7DpyCWO8hBRg2HYISJRWZhJ8Ea/1tj6xkPwb2aPwpsVmPbzMbz03WHkcZSHiBoAww4R6YV27vaIeT0UMwa2hYWZgF1nruCxRXH4vySO8hDRg2HYISK9YWEmQXjfVtj2xsPo5CWHsqwSb208hhfXJCK3kKM8RFQ/DDtEpHfausuwaVII3h7UFpZmEuxJvYrHPo/DxsNZHOUhojpj2CEivWRuJsHrj7TCb28+hEBvBxSVVWLG/x3HhDWJyCm8KXZ5RGRAGHaISK+1dpPhl9eCMXNwO1iaS7A39SoGLNqHnxM5ykNEtcOwQ0R6z9xMgtf6tMTvbz6Ezt4OKFJV4u1fjmPc6kRkF3CUh4jujWGHiAxGK1cZfpkUgncfrx7l2Xf2KgZ8vg/rD2VylIeI7ophh4gMiplEwCu9W+L3Nx9GFx8HFKsqMXPTCYxddQiXOcpDRDVg2CEig9TK1Q4bXwvBrCHtITWX4K9z1zDw831Yl8BRHiLSxbBDRAbLTCLgpYdbYPvkh9GtuSOKVZV4N+YExqw8hKz8UrHLIyI9wbBDRAavhYsdNrwajNlDO8DKQoL9569h0OJ9WHswA2o1R3mITB3DDhEZBTOJgIkP+WH75N7o7uuIkvIqzNqcgtErEzjKQ2TiRA07+/btw7Bhw+Dp6QlBELB582bttoqKCrzzzjsICAiAra0tPD09MXbsWGRnZ+scw9fXF4Ig6CzR0dFNfCZEpC/8nG2x4ZVgzB1WPcpzIO06Bi7ehx/i0znKQ2SiRA07JSUlCAwMxLJly+7YVlpaiiNHjmD27Nk4cuQINm3ahNTUVDzxxBN37Dtv3jzk5ORolzfeeKMpyiciPSWRCJgQ6ocdk3ujh58CpeVVmP3rSbzwv4PIvM5RHiJTYy7mhw8ePBiDBw+ucZtcLkdsbKzOui+//BI9evRAZmYmfHx8tOtlMhnc3d1r/bkqlQoqlUr7WqlU1rFyIjIEvs62WP9yL/xwMAPR28/g4IV8DFy8DzMHt8OYXs0hkQhil0hETcCgenYKCwshCAIcHBx01kdHR8PJyQlBQUH45JNPUFlZec/jREVFQS6Xaxdvb+9GrJqIxCSRCBgX4os/pvRGrxYK3KyowtwtJzHy24PIuF4idnlE1AQEjZ7ckEIQBMTExCAsLKzG7WVlZQgNDUW7du3w448/atcvWrQIXbp0gUKhwIEDBxAZGYkJEyZg0aJFd/2smkZ2vL29UVhYCHt7+wY7JyLSL2q1Bj8mZCBq+xmUllfB2sIMbw9qi3HBvhzlITJASqUScrn8vt/fBhF2KioqMHz4cFy6dAl79+695wmtWrUKr776KoqLiyGVSmv12bX9YRGRccjKL8Xb/3cc8ReuAwB6+Cqw8JlO8HW2FbkyIqqL2n5/6/1lrIqKCowYMQIZGRmIjY29bxjp2bMnKisrkZ6e3jQFEpHB8VbY4MeXeuKjMH/YWprhUHo+Bi3Zh5X7L6KKM7aIjI5eh53bQefcuXPYuXMnnJyc7vue5ORkSCQSuLq6NkGFRGSoJBIBo3s1x44pvRHaygllFWp8uO0UnvsmHheuFotdHhE1IFFnYxUXF+P8+fPa1xcvXkRycjIUCgU8PDzwzDPP4MiRI9i2bRuqqqqQm5sLAFAoFLC0tER8fDwSEhLQt29fyGQyxMfHY+rUqRg9ejQcHR3FOi0iMiDeChusndgTPx3Kwse/ncLhjBsYvOQvzBjYFhNC/WDGXh4igydqz87evXvRt2/fO9aPGzcO77//Pvz8/Gp83549e/DII4/gyJEjeP3113HmzBmoVCr4+flhzJgxmDZtWq37dQD27BBRtUs3ShG56QT+OncNANDFxwGfPBuIli52IldGRDUxuAZlMTHsENFtGo0GGxKz8NFvp1GsqoTUXILpA9pg4kMtOMpDpGeMpkGZiKgpCYKAkT188MfU3ujdxgWqSjXm/34Gzyw/gPNX2MtDZIgYdoiIatDMwRrfTeiOhcM7QSY1x9HMAjy+9C8sj0vjjC0iA8OwQ0R0F4IgYER3b/w5rTceaeuC8ko1orefwdNfH8C5vCKxyyOiWmLYISK6Dw+5NVaP745PnukEmZU5jmUVYMjS/fhq73lUVqnFLo+I7oNhh4ioFgRBwLPdvBE7tQ8ebeeK8io1Fu5IxfCvD+AsR3mI9BrDDhFRHbjLrbByXDd89mwg7K3McexSIYYu3Y95W08h/RofLEqkjzj1HJx6TkT1k6csw7ubTmDXmSvadb3buGBMr+Z4tJ0rp6oTNTLeZ6cOGHaIqL40Gg32nr2KH+IzsCf1Cm7/jdrMwRov9PTBc9294WxX+5ucElHtMezUAcMOETWEzOul+PFQBn5OzMKN0goAgKWZBI8HuGNMsC+6+DhAEDjaQ9RQGHbqgGGHiBpSWUUVfjueg+8PZuBYVoF2fQcPe4wNbo4nOnvCxlLURxMSGQWGnTpg2CGixnL8UgF+iM/AlmPZUFVWT1OXWZnjma5eGNOrOVrwuVtE9cawUwcMO0TU2ApKy7Hx8CWsTchAxvVS7fqHWztjdK/m6NfOFeZmnCBLVBcMO3XAsENETUWt1mDfuatYezADu87809DsKbe61dDsAxcZG5qJaoNhpw4YdohIDFn5pVh3KBMbErOQX1IOALAwEzDY3wNjgpujW3NHNjQT3QPDTh0w7BCRmMoqqrA9JQffx2fgaGaBdn07dxnGBDdHWOdmsJWyoZnovxh26oBhh4j0RcrlQvwQn4Ffj11GWcWthmapOYZ39cLoXs3RypUNzUS3MezUAcMOEembwtIKbEzKwtqDGUj/V0NzSEsnjA1ujv7t3djQTCaPYacOGHaISF+p1RrsP38NPxzMwK7TeVDf+hvb3b66oXlkd2+42luJWySRSBh26oBhh4gMweWCm1iXkIH1h7Jw/VZDs7lEwEB/d4zt1Rw9/BRsaCaTwrBTBww7RGRIVJVV2JGSi+/jM5CUcUO7vq2bDKODm+OpoGawY0MzmQCGnTpg2CEiQ3UyuxBrD2Zi89HLuFlRBQCwk5rj6S7NMLpXc7Rxk4lcIVHjYdipA4YdIjJ0hTcr8EvSJaw9mIEL10q063u1UGBML18M6OgGCzY0k5Fh2KkDhh0iMhYajQYH0q7j+/h0xJ76p6HZVSbF8z188EJPH7ixoZmMBMNOHTDsEJExyi64iZ8OZeKnQ1m4VqwCAJhJBAzs6IYxvXzRqwUbmsmwMezUAcMOERmz8ko1dpzMxQ/x6UhM/6ehubWrHcbcamiWWVmIWCFR/TDs1AHDDhGZitM5Sqw9mIGYo5dRWl7d0GxjaYangpphbLAv2rqzoZkMB8NOHTDsEJGpUZZVIObIZXwfn460q/80NLdxs0N3XwV6+CnQzVeBZg7WIlZJdG8MO3XAsENEpkqj0SD+wnX8EJ+BP0/loUqt+5XQzMEa3Xwd0c1XgR6+CrR2tYNEwj4f0g8MO3XAsENEBFwvViExPR+J6TeQmJ6Pk9nKO8KPvZU5uvkq0N1Xge6+jgjwkkNqbiZSxWTqGHbqgGGHiOhOJapKHM0sQGJ6Pg5n5ONIRoH2xoW3WZpL0NnLAd18HdHdT4EuPo6QW7PZmZqGQYSdffv24ZNPPkFSUhJycnIQExODsLAw7XaNRoO5c+fi22+/RUFBAUJDQ/H111+jdevW2n3y8/PxxhtvYOvWrZBIJBg+fDiWLFkCOzu7WtfBsENEdH8VVWqcylbeGv3Jx+H0G9pndN0mCNWPrbjd89PDVwF3Oe/rQ42jtt/foj48paSkBIGBgXjxxRfx9NNP37F94cKFWLp0Kb777jv4+flh9uzZGDhwIE6dOgUrq+r/eUaNGoWcnBzExsaioqICEyZMwCuvvIJ169Y19ekQERk1CzMJAr0dEOjtgJcebgGNRoML10pw+F+XvjKul+JMbhHO5Bbh+/gMAICXo/Wty17Vl75aurDvh5qW3lzGEgRBZ2RHo9HA09MT06dPx1tvvQUAKCwshJubG9asWYORI0fi9OnT6NChAxITE9GtWzcAwI4dO/D444/j0qVL8PT0rNVnc2SHiKhhXFGWaYPP4Yx8nMpW4j9tP3C0sUDX5tXBp7ufAv6eclia81EWVHcGMbJzLxcvXkRubi769++vXSeXy9GzZ0/Ex8dj5MiRiI+Ph4ODgzboAED//v0hkUiQkJCAp556qsZjq1QqqFQq7WulUtl4J0JEZEJc7a0wpJMHhnTyAAAUlVVo+34S0/ORnFWAG6UV2Hk6DztP5wEApOYSdPZ20F766uLjwJscUoPS27CTm5sLAHBzc9NZ7+bmpt2Wm5sLV1dXne3m5uZQKBTafWoSFRWFDz74oIErJiKi/5JZWaB3Gxf0buMCoPpuzinZhdpLX4fT83GjtAIJF/ORcDEfACARgPYe9jqXvlz5PC96AHobdhpTZGQkpk2bpn2tVCrh7e0tYkVERKbB0lyCLj6O6OLjiFd6A2q1BheuFePQxergk5iRj6z8mziZrcTJbCXWHEgHAPgobLTBp5uvAi1dbPlcL6o1vQ077u7uAIC8vDx4eHho1+fl5aFz587afa5cuaLzvsrKSuTn52vfXxOpVAqpVNrwRRMRUZ1IJAJaucrQylWGF3r6AAByC8tuzfbKx6H0GziTq0Rmfiky80vxy5FLAACFrSW6NXfUXvrq6GkPCzP2/VDN9Dbs+Pn5wd3dHbt27dKGG6VSiYSEBEyaNAkAEBwcjIKCAiQlJaFr164AgN27d0OtVqNnz55ilU5ERA/AXW6FYYGeGBZYPclEWVaBpIwb2ktfyVkFyC8px5+n8vDnqeq+H2sLMwT5ONy64aEjujZ3hI2l3n7FURMT9b+E4uJinD9/Xvv64sWLSE5OhkKhgI+PD6ZMmYKPPvoIrVu31k499/T01M7Yat++PQYNGoSXX34Zy5cvR0VFBSIiIjBy5Mhaz8QiIiL9Zm9lgb5tXdG3bXWPpqqyCimXC6tnfV3Mx+GMGyi8WYEDaddxIO06gOrw82h7Vwzr5IFH2rrCyoJ3eTZlok4937t3L/r27XvH+nHjxmHNmjXamwquWLECBQUFeOihh/DVV1+hTZs22n3z8/MRERGhc1PBpUuX8qaCREQmQq3W4NyVYu2lr4SL+cgpLNNut7U0w2Md3DC0kycebuPMx1sYEYO4g7K+YNghIjIeGo0Gxy8VYtvxbPx2PAfZ/wo+MitzDOzojqGdPBDaypl9PgaOYacOGHaIiIyTWq3B0awb2HosB7+fyMGVon/useZoY4FB/u4Y2skTvVo4wYx3dTY4DDt1wLBDRGT8qtQaJKbnY9vxbGw/kavzXC9nO0sM9vfA0E4e6O6r4OMsDATDTh0w7BARmZbKKjUOXqgOPjtO5qKgtEK7zd3eCo8HeGBooAeCvB14Px89xrBTBww7RESmq6JKjf3nr2HbsRz8eTIXRapK7bZmDtYY2skDQzt5wr+ZPYOPnmHYqQOGHSIiAqqnte87ew3bjmdj56k8lJRXabc1d7LRBp927jIGHz3AsFMHDDtERPRfZRVV2HPmCrYdz8GuM3koq1Brt7V0scXQTp4YFuiBVq4yEas0bQw7dcCwQ0RE91KiqsSuM1ew7Vg29p69ivLKf4JPO3eZdsTH19lWxCpND8NOHTDsEBFRbSnLKrDzVB62HsvGX+euoVL9z9eofzN7DO3kiSEBHvBW2IhYpWlg2KkDhh0iIqqPgtJy/HEyF9uO5+BA2nVU/Sv4dPZ2wLDA6uDjLrcSsUrjxbBTBww7RET0oK4Xq7A9JRfbjmcj4WI+bn+7CgLQvbkCQwM9MNjfAy4yqbiFGhGGnTpg2CEiooZ0RVmG30/kYNvxHBzOuKFdLxGAXi2cMLSTJwb5u0NhaylilYaPYacOGHaIiKixZBfcxO8ncrD1eA6OZRVo15tJBIS2csbQTh4Y2MEdchsL8Yo0UAw7dcCwQ0RETSErvxTbjudg2/FsnMxWatdbmAno3doFQwM90L+9G2RWDD61wbBTBww7RETU1C5cLcZvx6svdaXmFWnXW5pL0LetC4Z28kS/9q6wsTQXsUr9xrBTBww7REQkpnN5Rdh6a8TnwtUS7XoLMwFBPo4IaemEkJbO6OztAEtziYiV6heGnTpg2CEiIn2g0WhwOqcI245n47cTOci4Xqqz3drCDN39FAhp6YTQls7o4GkPMxN+QjvDTh0w7BARkb7RaDTIzC/F3+ev40DaNcSnXcf1knKdfeTWFujVQoGQls4IaemEVq52JvXMLoadOmDYISIifafRaJCaV4QDt8JPwoV8nSe0A4CLTHrrklf1ZS9jv4szw04dMOwQEZGhqaxSIyVbib/PV4/6JKbnQ/WvZ3YBgLfCGiEtnBHSygnBLZ3gKjOuOzkz7NQBww4RERk6VWUVjmQUID7tGg6kXUdyVoHOc7sAoLWrHUJbOSO4pRN6tXCC3Nqwp7gz7NQBww4RERmbYlUlEtPzceB8dfg5laPEv7/xJQLg30yO4FvNzt18HQ1umjvDTh0w7BARkbG7UVKOgxeu40Badc9P2r+muAO3prl7OyKkleFMc2fYqQOGHSIiMjW5hWWIv3DtVsPzdVwuuKmz/d/T3ENaOqGjp1zvprkz7NQBww4REZmy29PcD6Rd1zY8/3eau72VOXq1cEJoK/2Z5s6wUwcMO0RERP/QaDQ4m1eMv2/1+yRcuK6X09wZduqAYYeIiOjubk9zv31zw8T0fJRViD/NnWGnDhh2iIiIak9VWYWjmQXamV53m+Ye0tIJIa2c0cvPCXKbhp/mzrBTBww7RERE9VeiqsSh9HzE35rpdTJbd5q7IAA/vdwLvVo4Nejn1vb727Am1BMREZHesZWao29bV/Rt6wqgepp7wsXr2ud6ZVwvRUdP8QYTGHaIiIioQTnaWmKQvwcG+XsAAPJLyiGzEu9uzfp9tyAAvr6+EAThjiU8PBwA8Mgjj9yx7bXXXhO5aiIiIrpNYWsp6ufr/chOYmIiqqqqtK9TUlLw2GOP4dlnn9Wue/nllzFv3jztaxsb437KKxEREdWe3ocdFxcXndfR0dFo2bIl+vTpo11nY2MDd3f3pi6NiIiIDIDeX8b6t/LycqxduxYvvviizl0bf/zxRzg7O8Pf3x+RkZEoLS2953FUKhWUSqXOQkRERMZJ70d2/m3z5s0oKCjA+PHjteteeOEFNG/eHJ6enjh+/DjeeecdpKamYtOmTXc9TlRUFD744IMmqJiIiIjEZlD32Rk4cCAsLS2xdevWu+6ze/du9OvXD+fPn0fLli1r3EelUkGlUmlfK5VKeHt78z47REREBsTo7rOTkZGBnTt33nPEBgB69uwJAPcMO1KpFFKptMFrJCIiIv1jMD07q1evhqurK4YMGXLP/ZKTkwEAHh4eTVAVERER6TuDGNlRq9VYvXo1xo0bB3Pzf0pOS0vDunXr8Pjjj8PJyQnHjx/H1KlT0bt3b3Tq1EnEiomIiEhfGETY2blzJzIzM/Hiiy/qrLe0tMTOnTuxePFilJSUwNvbG8OHD8esWbNEqpSIiIj0jUE1KDcWPgiUiIjI8NT2+9tgenaIiIiI6oNhh4iIiIwaww4REREZNYNoUG5st9uW+NgIIiIiw3H7e/t+7ccMOwCKiooAAN7e3iJXQkRERHVVVFQEuVx+1+2cjYXq+/hkZ2dDJpPpPGD0Qd1+DEVWVhZneekB/j70D38n+oW/D/3C38f9aTQaFBUVwdPTExLJ3TtzOLIDQCKRwMvLq9GOb29vz/9Q9Qh/H/qHvxP9wt+HfuHv497uNaJzGxuUiYiIyKgx7BAREZFRY9hpRFKpFHPnzuUT1vUEfx/6h78T/cLfh37h76PhsEGZiIiIjBpHdoiIiMioMewQERGRUWPYISIiIqPGsENERERGjWGnEURFRaF79+6QyWRwdXVFWFgYUlNTxS6LbomOjoYgCJgyZYrYpZisy5cvY/To0XBycoK1tTUCAgJw+PBhscsySVVVVZg9ezb8/PxgbW2Nli1b4sMPP7zvs4ao4ezbtw/Dhg2Dp6cnBEHA5s2bdbZrNBrMmTMHHh4esLa2Rv/+/XHu3DlxijVQDDuNIC4uDuHh4Th48CBiY2NRUVGBAQMGoKSkROzSTF5iYiK++eYbdOrUSexSTNaNGzcQGhoKCwsLbN++HadOncJnn30GR0dHsUszSQsWLMDXX3+NL7/8EqdPn8aCBQuwcOFCfPHFF2KXZjJKSkoQGBiIZcuW1bh94cKFWLp0KZYvX46EhATY2tpi4MCBKCsra+JKDRennjeBq1evwtXVFXFxcejdu7fY5Zis4uJidOnSBV999RU++ugjdO7cGYsXLxa7LJMzc+ZM/P333/jrr7/ELoUADB06FG5ubli5cqV23fDhw2FtbY21a9eKWJlpEgQBMTExCAsLA1A9quPp6Ynp06fjrbfeAgAUFhbCzc0Na9aswciRI0Ws1nBwZKcJFBYWAgAUCoXIlZi28PBwDBkyBP379xe7FJO2ZcsWdOvWDc8++yxcXV0RFBSEb7/9VuyyTFZISAh27dqFs2fPAgCOHTuG/fv3Y/DgwSJXRgBw8eJF5Obm6vy9JZfL0bNnT8THx4tYmWHhg0AbmVqtxpQpUxAaGgp/f3+xyzFZ69evx5EjR5CYmCh2KSbvwoUL+PrrrzFt2jS8++67SExMxJtvvglLS0uMGzdO7PJMzsyZM6FUKtGuXTuYmZmhqqoKH3/8MUaNGiV2aQQgNzcXAODm5qaz3s3NTbuN7o9hp5GFh4cjJSUF+/fvF7sUk5WVlYXJkycjNjYWVlZWYpdj8tRqNbp164b58+cDAIKCgpCSkoLly5cz7Ijg559/xo8//oh169ahY8eOSE5OxpQpU+Dp6cnfBxkNXsZqRBEREdi2bRv27NkDLy8vscsxWUlJSbhy5Qq6dOkCc3NzmJubIy4uDkuXLoW5uTmqqqrELtGkeHh4oEOHDjrr2rdvj8zMTJEqMm0zZszAzJkzMXLkSAQEBGDMmDGYOnUqoqKixC6NALi7uwMA8vLydNbn5eVpt9H9Mew0Ao1Gg4iICMTExGD37t3w8/MTuyST1q9fP5w4cQLJycnapVu3bhg1ahSSk5NhZmYmdokmJTQ09I5bMZw9exbNmzcXqSLTVlpaColE96vAzMwMarVapIro3/z8/ODu7o5du3Zp1ymVSiQkJCA4OFjEygwLL2M1gvDwcKxbtw6//vorZDKZ9rqqXC6HtbW1yNWZHplMdke/lK2tLZycnNhHJYKpU6ciJCQE8+fPx4gRI3Do0CGsWLECK1asELs0kzRs2DB8/PHH8PHxQceOHXH06FEsWrQIL774otilmYzi4mKcP39e+/rixYtITk6GQqGAj48PpkyZgo8++gitW7eGn58fZs+eDU9PT+2MLaoFDTU4ADUuq1evFrs0uqVPnz6ayZMni12Gydq6davG399fI5VKNe3atdOsWLFC7JJMllKp1EyePFnj4+OjsbKy0rRo0ULz3nvvaVQqldilmYw9e/bU+J0xbtw4jUaj0ajVas3s2bM1bm5uGqlUqunXr58mNTVV3KINDO+zQ0REREaNPTtERERk1Bh2iIiIyKgx7BAREZFRY9ghIiIio8awQ0REREaNYYeIiIiMGsMOERERGTWGHSIiIjJqDDtE1KjS09MhCAKSk5PFLkXrzJkz6NWrF6ysrNC5c+c6v18fz4mI7o5hh8jIjR8/HoIgIDo6Wmf95s2bIQiCSFWJa+7cubC1tUVqaqrOAxbFsmbNGjg4OIhdBpHRYtghMgFWVlZYsGABbty4IXYpDaa8vLze701LS8NDDz2E5s2bw8nJqQGrEldVVRWfVk5UA4YdIhPQv39/uLu7Iyoq6q77vP/++3dc0lm8eDF8fX21r8ePH4+wsDDMnz8fbm5ucHBwwLx581BZWYkZM2ZAoVDAy8sLq1evvuP4Z86cQUhICKysrODv74+4uDid7SkpKRg8eDDs7Ozg5uaGMWPG4Nq1a9rtjzzyCCIiIjBlyhQ4Oztj4MCBNZ6HWq3GvHnz4OXlBalUis6dO2PHjh3a7YIgICkpCfPmzYMgCHj//ffvepyFCxeiVatWkEql8PHxwccff1zjvjWNzPx35OzYsWPo27cvZDIZ7O3t0bVrVxw+fBh79+7FhAkTUFhYCEEQdGpSqVR466230KxZM9ja2qJnz57Yu3fvHZ+7ZcsWdOjQAVKpFJmZmdi7dy969OgBW1tbODg4IDQ0FBkZGTXWTmQKGHaITICZmRnmz5+PL774ApcuXXqgY+3evRvZ2dnYt28fFi1ahLlz52Lo0KFwdHREQkICXnvtNbz66qt3fM6MGTMwffp0HD16FMHBwRg2bBiuX78OACgoKMCjjz6KoKAgHD58GDt27EBeXh5GjBihc4zvvvsOlpaW+Pvvv7F8+fIa61uyZAk+++wzfPrppzh+/DgGDhyIJ554AufOnQMA5OTkoGPHjpg+fTpycnLw1ltv1XicyMhIREdHY/bs2Th16hTWrVsHNze3ev/cRo0aBS8vLyQmJiIpKQkzZ86EhYUFQkJCsHjxYtjb2yMnJ0enpoiICMTHx2P9+vU4fvw4nn32WQwaNEh7LgBQWlqKBQsW4H//+x9OnjwJhUKBsLAw9OnTB8ePH0d8fDxeeeUVk71kSQQAEPux60TUuMaNG6d58sknNRqNRtOrVy/Niy++qNFoNJqYmBjNv/8KmDt3riYwMFDnvZ9//rmmefPmOsdq3ry5pqqqSruubdu2mocfflj7urKyUmNra6v56aefNBqNRnPx4kUNAE10dLR2n4qKCo2Xl5dmwYIFGo1Go/nwww81AwYM0PnsrKwsDQBNamqqRqPRaPr06aMJCgq67/l6enpqPv74Y5113bt317z++uva14GBgZq5c+fe9RhKpVIjlUo13377bY3bb5/T0aNHNRqNRrN69WqNXC7X2ee/P1+ZTKZZs2ZNjcer6f0ZGRkaMzMzzeXLl3XW9+vXTxMZGal9HwBNcnKydvv169c1ADR79+696/kRmRqO7BCZkAULFuC7777D6dOn632Mjh07QiL5568ONzc3BAQEaF+bmZnByckJV65c0XlfcHCw9s/m5ubo1q2bto5jx45hz549sLOz0y7t2rUDUN1fc1vXrl3vWZtSqUR2djZCQ0N11oeGhtbpnE+fPg2VSoV+/frV+j33M23aNLz00kvo378/oqOjdc6rJidOnEBVVRXatGmj83OJi4vTea+lpSU6deqkfa1QKDB+/HgMHDgQw4YNw5IlS5CTk9Ng50FkiBh2iExI7969MXDgQERGRt6xTSKRQKPR6KyrqKi4Yz8LCwud14Ig1LiuLo2yxcXFGDZsGJKTk3WWc+fOoXfv3tr9bG1ta33MB2FtbV2n/Wvzs3v//fdx8uRJDBkyBLt370aHDh0QExNz12MWFxfDzMwMSUlJOj+T06dPY8mSJTq1/vcS1erVqxEfH4+QkBBs2LABbdq0wcGDB+t0TkTGhGGHyMRER0dj69atiI+P11nv4uKC3NxcnS/thryPzL+/bCsrK5GUlIT27dsDALp06YKTJ0/C19cXrVq10lnqEnDs7e3h6emJv//+W2f933//jQ4dOtT6OK1bt4a1tXWtp6W7uLigqKgIJSUl2nU1/ezatGmDqVOn4s8//8TTTz+tbeS2tLREVVWVzr5BQUGoqqrClStX7viZuLu737emoKAgREZG4sCBA/D398e6detqdS5Exohhh8jEBAQEYNSoUVi6dKnO+kceeQRXr17FwoULkZaWhmXLlmH79u0N9rnLli1DTEwMzpw5g/DwcNy4cQMvvvgiACA8PBz5+fl4/vnnkZiYiLS0NPzxxx+YMGHCHSHgfmbMmIEFCxZgw4YNSE1NxcyZM5GcnIzJkyfX+hhWVlZ455138Pbbb+P7779HWloaDh48iJUrV9a4f8+ePWFjY4N3330XaWlpWLduHdasWaPdfvPmTURERGDv3r3IyMjA33//jcTERG3Y8/X1RXFxMXbt2oVr166htLQUbdq0wahRozB27Fhs2rQJFy9exKFDhxAVFYXffvvtrrVfvHgRkZGRiI+PR0ZGBv7880+cO3dO+1lEpohhh8gEzZs3747LTO3bt8dXX32FZcuWITAwEIcOHbrrTKX6iI6ORnR0NAIDA7F//35s2bIFzs7OAKAdjamqqsKAAQMQEBCAKVOmwMHBQac/qDbefPNNTJs2DdOnT0dAQAB27NiBLVu2oHXr1nU6zuzZszF9+nTMmTMH7du3x3PPPXdHH9JtCoUCa9euxe+//46AgAD89NNPOlPazczMcP36dYwdOxZt2rTBiBEjMHjwYHzwwQcAgJCQELz22mt47rnn4OLigoULFwKovhw1duxYTJ8+HW3btkVYWBgSExPh4+Nz17ptbGxw5swZDB8+HG3atMErr7yC8PBwvPrqq3U6fyJjImj+e6GZiIiIyIhwZIeIiIiMGsMOERERGTWGHSIiIjJqDDtERERk1Bh2iIiIyKgx7BAREZFRY9ghIiIio8awQ0REREaNYYeIiIiMGsMOERERGTWGHSIiIjJq/w9/yZ0p3YasmwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wow!, The elbow of the intertia curves is around 3 cluster centres"
      ],
      "metadata": {
        "id": "T3zo8WnPKnAy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***We want to use the Silhouette Score hyperparater to verfiy the the cluster centres. We do so by creating a function to calculate the silhouette scores***"
      ],
      "metadata": {
        "id": "pBiYtp_OLHzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We write a function to return a list of each k-value's score.\n",
        "def kmeans_sil(num_clusters, x_vals):\n",
        "    \"\"\"\n",
        "    Accepts as arguments list of ints and data array.\n",
        "    Fits a KMeans model where k = each value in the list of ints.\n",
        "    Calculates a silhouette score for each k value.\n",
        "    Returns each k-value's silhouette score appended to a list.\n",
        "    \"\"\"\n",
        "    sil_score = []\n",
        "    for num in num_clusters:\n",
        "        kms = KMeans(n_clusters=num,max_iter = 1000, random_state=42)\n",
        "        kms.fit(x_vals)\n",
        "        sil_score.append(silhouette_score(x_vals, kms.labels_))\n",
        "\n",
        "    return sil_score\n",
        "\n",
        "\n",
        "sil_score = kmeans_sil(num_clusters, X_scaled)\n",
        "sil_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYgD1yimF5uy",
        "outputId": "c4c635e5-4403-4467-82b2-46d9580010ec"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.24064683432964232,\n",
              " 0.2490753796988128,\n",
              " 0.2853204754177715,\n",
              " 0.31452049069545956,\n",
              " 0.3198416477495805,\n",
              " 0.3242528404198317,\n",
              " 0.3158689556344613,\n",
              " 0.32614142907516724,\n",
              " 0.3267392344081264,\n",
              " 0.30425054604829005]"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a line plot for the silhouette score.\n",
        "\n",
        "\n",
        "plot = sns.lineplot(x=num_clusters, y=sil_score)\n",
        "plot.set_xlabel(\"# of clusters\");\n",
        "plot.set_ylabel(\"Silhouette Score\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "oR_bM2TbGFg2",
        "outputId": "b6a68c3f-acf8-4a92-eb83-ea2f6675db80"
      },
      "execution_count": 214,
      "outputs": [
        {
          "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": [
        "Congratulatings to us. We find that the optimal value of the Silhoutte score is 3. So we have 3 cluster centres"
      ],
      "metadata": {
        "id": "n2QP0c9oL57-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*** We now create the model with 3 cluster centres, and fit it to our X_scaled data***"
      ],
      "metadata": {
        "id": "5IljFcZSMGXt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit a 3-cluster model.\n",
        "\n",
        "model = KMeans(n_clusters = 7, max_iter = 1000, random_state = 42)\n",
        "model.fit(X_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "y7ws9i-KGXMH",
        "outputId": "b5c6ea38-a321-47ea-a699-4fd03c99b86f"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(max_iter=1000, n_clusters=7, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-32 {color: black;background-color: white;}#sk-container-id-32 pre{padding: 0;}#sk-container-id-32 div.sk-toggleable {background-color: white;}#sk-container-id-32 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-32 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-32 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-32 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-32 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-32 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-32 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-32 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-32 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-32 div.sk-item {position: relative;z-index: 1;}#sk-container-id-32 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-32 div.sk-item::before, #sk-container-id-32 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-32 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-32 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-32 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-32 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-32 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-32 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-32 div.sk-label-container {text-align: center;}#sk-container-id-32 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-32 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-32\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=1000, n_clusters=7, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" checked><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(max_iter=1000, n_clusters=7, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can now print the unique labels\n",
        "print(\"Unique labels: \", np.unique(model.labels_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLK_yDVDGn99",
        "outputId": "a677f0a2-84e1-42c2-dbb5-f4955d8d5c2a"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique labels:  [0 1 2 3 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*** Now, Lets add a new column called clusters to our derivative reflectanceand get the sizes of each cluster***"
      ],
      "metadata": {
        "id": "yvDZDE1AMtal"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "components['cluster'] = model.labels_"
      ],
      "metadata": {
        "id": "Fkxc9hlaGx4b"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #We want to verify the size of the cluster`.\n",
        "\n",
        "components.groupby(by = ['cluster']).size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lsXBJ-bHscq",
        "outputId": "5d77c564-3eb0-4862-e045-7ca2d49e9331"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cluster\n",
              "0    15\n",
              "1     7\n",
              "2    16\n",
              "3     4\n",
              "4     5\n",
              "5     9\n",
              "6     5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We want to plot the sizes of the cluster centers\n",
        "components.groupby(by=['cluster']).size().plot.bar(title='Clusters differentiated by VPCA of derivative spectra',\n",
        "                                                                   figsize=(6, 5),\n",
        "                                                                   ylabel='Size',\n",
        "                                                                   xlabel='(Cluster)');\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "-yLUKWZxIhU4",
        "outputId": "9a6895af-80b1-4ed2-a92d-5ca6b8456faa"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in np.unique(model.labels_):\n",
        "  plt.scatter(components[components['cluster'] == i]['6VPCA Score 1' ],components[components['cluster'] == i]['6VPCA Score 2' ] )\n",
        "  #plt.scatter(components['cluster' == i, 1], components['cluster' == i, 2], cluster = i) # idf[label == i , 0] , df[label == i , 1] , label = i)\n",
        "plt.xlabel('VPCA Score 1')\n",
        "plt.ylabel('VPCA Score 2')\n",
        "plt.legend(labels = ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5', 'Cluster 6', 'Cluster 7' ])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Csq8_2Z1uVdK",
        "outputId": "e81d8507-f3f0-44fe-8608-1456c408de9b"
      },
      "execution_count": 320,
      "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": [
        "# k= [ 0,2,3, 4, 6, 7, 8, 9, 10]\n",
        "# for i in k:\n",
        "#   plt.scatter(components[components['cluster'] == i]['6VPCA Score 1' ],components[components['cluster'] == i]['6VPCA Score 2' ] )\n",
        "#   #plt.scatter(components['cluster' == i, 1], components['cluster' == i, 2], cluster = i) # idf[label == i , 0] , df[label == i , 1] , label = i)\n",
        "# plt.legend()\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "OoOtg4ZPdivV"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_1 = components[components['cluster']==0]\n",
        "Cluster_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "8gumxa31Sf3i",
        "outputId": "3a80e173-c546-4301-d54f-cffe5838f19f"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "3       JB_5_Corn_Res_derivative        0.22135        0.59156       -0.27654   \n",
              "5       JB_3_Corn_Res_derivative        0.10088        0.33024       -0.12889   \n",
              "11   LynnW_05_CornRes_derivative        0.13039        0.47146       -1.06606   \n",
              "14   LynnW_02_CornRes_derivative        0.02326        0.36264       -1.17524   \n",
              "16       KB_4_Dry_Rye_derivative        0.10936        0.27445       -0.98474   \n",
              "25  JanD_S8_Bare_Soil_derivative        0.03192        0.64042       -0.81841   \n",
              "30      LynnW_05_Soil_derivative       -0.03934        0.17518       -0.63764   \n",
              "31      LynnW_04_Soil_derivative       -0.04935        0.14814       -0.59636   \n",
              "32      LynnW_03_Soil_derivative       -0.12356        0.11297       -0.57648   \n",
              "33      LynnW_02_Soil_derivative       -0.06290        0.09099       -0.73817   \n",
              "39          SK_1_Soil_derivative       -0.23491        0.02016       -1.08362   \n",
              "40          KB_5_Soil_derivative        0.03470        0.70528       -0.88410   \n",
              "41          KB_4_Soil_derivative       -0.19888        0.03979       -0.94479   \n",
              "42          KB_3_Soil_derivative        0.18556        0.97270       -0.23655   \n",
              "43          KB_1_Soil_derivative        0.01915        0.72211       -0.73923   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "3        -0.63017        0.09259        0.34682        0  \n",
              "5        -0.98110       -0.49311        0.21719        0  \n",
              "11       -0.41610        0.06507        0.18301        0  \n",
              "14       -0.62427        0.04059        0.55469        0  \n",
              "16        0.30089       -0.94334        0.96870        0  \n",
              "25        0.35069       -0.38309       -0.13945        0  \n",
              "30       -0.48317       -0.99343       -0.25075        0  \n",
              "31       -0.55109       -0.23841       -1.18819        0  \n",
              "32       -0.91078        0.14304       -1.42339        0  \n",
              "33       -0.32865       -1.09834       -0.64372        0  \n",
              "39        0.26146       -0.91090       -0.88448        0  \n",
              "40        0.37965        0.57783       -0.70456        0  \n",
              "41       -1.09063       -0.37563       -0.25845        0  \n",
              "42        0.55060        0.80060       -0.77671        0  \n",
              "43        0.48231        0.63664       -0.94430        0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12abae18-80dd-40d6-9ffe-488a1cd61023\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\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>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JB_5_Corn_Res_derivative</td>\n",
              "      <td>0.22135</td>\n",
              "      <td>0.59156</td>\n",
              "      <td>-0.27654</td>\n",
              "      <td>-0.63017</td>\n",
              "      <td>0.09259</td>\n",
              "      <td>0.34682</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>JB_3_Corn_Res_derivative</td>\n",
              "      <td>0.10088</td>\n",
              "      <td>0.33024</td>\n",
              "      <td>-0.12889</td>\n",
              "      <td>-0.98110</td>\n",
              "      <td>-0.49311</td>\n",
              "      <td>0.21719</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LynnW_05_CornRes_derivative</td>\n",
              "      <td>0.13039</td>\n",
              "      <td>0.47146</td>\n",
              "      <td>-1.06606</td>\n",
              "      <td>-0.41610</td>\n",
              "      <td>0.06507</td>\n",
              "      <td>0.18301</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LynnW_02_CornRes_derivative</td>\n",
              "      <td>0.02326</td>\n",
              "      <td>0.36264</td>\n",
              "      <td>-1.17524</td>\n",
              "      <td>-0.62427</td>\n",
              "      <td>0.04059</td>\n",
              "      <td>0.55469</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>KB_4_Dry_Rye_derivative</td>\n",
              "      <td>0.10936</td>\n",
              "      <td>0.27445</td>\n",
              "      <td>-0.98474</td>\n",
              "      <td>0.30089</td>\n",
              "      <td>-0.94334</td>\n",
              "      <td>0.96870</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>JanD_S8_Bare_Soil_derivative</td>\n",
              "      <td>0.03192</td>\n",
              "      <td>0.64042</td>\n",
              "      <td>-0.81841</td>\n",
              "      <td>0.35069</td>\n",
              "      <td>-0.38309</td>\n",
              "      <td>-0.13945</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>LynnW_05_Soil_derivative</td>\n",
              "      <td>-0.03934</td>\n",
              "      <td>0.17518</td>\n",
              "      <td>-0.63764</td>\n",
              "      <td>-0.48317</td>\n",
              "      <td>-0.99343</td>\n",
              "      <td>-0.25075</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>LynnW_04_Soil_derivative</td>\n",
              "      <td>-0.04935</td>\n",
              "      <td>0.14814</td>\n",
              "      <td>-0.59636</td>\n",
              "      <td>-0.55109</td>\n",
              "      <td>-0.23841</td>\n",
              "      <td>-1.18819</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>LynnW_03_Soil_derivative</td>\n",
              "      <td>-0.12356</td>\n",
              "      <td>0.11297</td>\n",
              "      <td>-0.57648</td>\n",
              "      <td>-0.91078</td>\n",
              "      <td>0.14304</td>\n",
              "      <td>-1.42339</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>LynnW_02_Soil_derivative</td>\n",
              "      <td>-0.06290</td>\n",
              "      <td>0.09099</td>\n",
              "      <td>-0.73817</td>\n",
              "      <td>-0.32865</td>\n",
              "      <td>-1.09834</td>\n",
              "      <td>-0.64372</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>SK_1_Soil_derivative</td>\n",
              "      <td>-0.23491</td>\n",
              "      <td>0.02016</td>\n",
              "      <td>-1.08362</td>\n",
              "      <td>0.26146</td>\n",
              "      <td>-0.91090</td>\n",
              "      <td>-0.88448</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>KB_5_Soil_derivative</td>\n",
              "      <td>0.03470</td>\n",
              "      <td>0.70528</td>\n",
              "      <td>-0.88410</td>\n",
              "      <td>0.37965</td>\n",
              "      <td>0.57783</td>\n",
              "      <td>-0.70456</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>KB_4_Soil_derivative</td>\n",
              "      <td>-0.19888</td>\n",
              "      <td>0.03979</td>\n",
              "      <td>-0.94479</td>\n",
              "      <td>-1.09063</td>\n",
              "      <td>-0.37563</td>\n",
              "      <td>-0.25845</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>KB_3_Soil_derivative</td>\n",
              "      <td>0.18556</td>\n",
              "      <td>0.97270</td>\n",
              "      <td>-0.23655</td>\n",
              "      <td>0.55060</td>\n",
              "      <td>0.80060</td>\n",
              "      <td>-0.77671</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>KB_1_Soil_derivative</td>\n",
              "      <td>0.01915</td>\n",
              "      <td>0.72211</td>\n",
              "      <td>-0.73923</td>\n",
              "      <td>0.48231</td>\n",
              "      <td>0.63664</td>\n",
              "      <td>-0.94430</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <style>\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-12abae18-80dd-40d6-9ffe-488a1cd61023 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-12abae18-80dd-40d6-9ffe-488a1cd61023');\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",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b3b140fc-4735-49f3-af72-9a6d88b7728f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_2 = components[components['cluster']==1]\n",
        "Cluster_2"
      ],
      "metadata": {
        "id": "i3dz7ERgQ_iK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "4dc2b907-e76e-4df8-affc-c417f64d3d0f"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "6    JB_2_Corn_Res_derivative        0.68073        0.99558        1.91077   \n",
              "24       JB_0_Road_derivative        0.82569        0.55711        1.75469   \n",
              "44       JB_5_Soil_derivative        0.33195        0.37671        1.08634   \n",
              "45       JB_4_Soil_derivative        0.48866        0.49995        1.99945   \n",
              "47       JB_2_Soil_derivative        0.69563        0.95687        2.47929   \n",
              "48       JB_1_Soil_derivative        0.69685        0.97202        2.78105   \n",
              "49  JB_1_Soya_Bean_derivative        0.34731       -0.34267        1.67154   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "6        -0.37851        0.29252       -0.04124        1  \n",
              "24       -0.35000       -2.29446        2.39378        1  \n",
              "44       -1.02188       -1.07993        0.21160        1  \n",
              "45       -0.81329       -1.53388        0.30797        1  \n",
              "47       -0.66567        0.13843       -0.20964        1  \n",
              "48       -0.76941        0.22750       -0.39316        1  \n",
              "49       -0.59478        0.44177        0.38146        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a26bb606-2d29-40c1-9712-99a57afd253d\" class=\"colab-df-container\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>JB_2_Corn_Res_derivative</td>\n",
              "      <td>0.68073</td>\n",
              "      <td>0.99558</td>\n",
              "      <td>1.91077</td>\n",
              "      <td>-0.37851</td>\n",
              "      <td>0.29252</td>\n",
              "      <td>-0.04124</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>JB_0_Road_derivative</td>\n",
              "      <td>0.82569</td>\n",
              "      <td>0.55711</td>\n",
              "      <td>1.75469</td>\n",
              "      <td>-0.35000</td>\n",
              "      <td>-2.29446</td>\n",
              "      <td>2.39378</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>JB_5_Soil_derivative</td>\n",
              "      <td>0.33195</td>\n",
              "      <td>0.37671</td>\n",
              "      <td>1.08634</td>\n",
              "      <td>-1.02188</td>\n",
              "      <td>-1.07993</td>\n",
              "      <td>0.21160</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>JB_4_Soil_derivative</td>\n",
              "      <td>0.48866</td>\n",
              "      <td>0.49995</td>\n",
              "      <td>1.99945</td>\n",
              "      <td>-0.81329</td>\n",
              "      <td>-1.53388</td>\n",
              "      <td>0.30797</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>JB_2_Soil_derivative</td>\n",
              "      <td>0.69563</td>\n",
              "      <td>0.95687</td>\n",
              "      <td>2.47929</td>\n",
              "      <td>-0.66567</td>\n",
              "      <td>0.13843</td>\n",
              "      <td>-0.20964</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>JB_1_Soil_derivative</td>\n",
              "      <td>0.69685</td>\n",
              "      <td>0.97202</td>\n",
              "      <td>2.78105</td>\n",
              "      <td>-0.76941</td>\n",
              "      <td>0.22750</td>\n",
              "      <td>-0.39316</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>JB_1_Soya_Bean_derivative</td>\n",
              "      <td>0.34731</td>\n",
              "      <td>-0.34267</td>\n",
              "      <td>1.67154</td>\n",
              "      <td>-0.59478</td>\n",
              "      <td>0.44177</td>\n",
              "      <td>0.38146</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        document.querySelector('#df-a26bb606-2d29-40c1-9712-99a57afd253d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a26bb606-2d29-40c1-9712-99a57afd253d');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-7a671c83-fd16-4575-b47b-265975565b0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a671c83-fd16-4575-b47b-265975565b0a')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a671c83-fd16-4575-b47b-265975565b0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_3 = components[components['cluster']==2]\n",
        "Cluster_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "qT60fBmNS_A8",
        "outputId": "ad6748e5-b520-4b80-d269-759a351c1188"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "17   JB_Grass_Filt_derivative       -0.24803       -1.43721       -0.31468   \n",
              "18      SK_5_Grass_derivative       -0.20154       -1.63808       -0.08945   \n",
              "19      SK_4_Grass_derivative       -0.10447       -1.75455        0.28321   \n",
              "20      SK_3_Grass_derivative       -0.17191       -1.62548       -0.25792   \n",
              "21      SK_2_Grass_derivative       -0.28412       -1.36299       -0.57340   \n",
              "22      SK_1_Grass_derivative       -0.26351       -1.43892       -0.41211   \n",
              "23   ZachA_12_Road_derivative       -0.15460       -1.26814       -0.35687   \n",
              "50    JanD_S6_Weed_derivative        0.04317       -1.23126        0.60679   \n",
              "52  JanD_S6_Weed_3_derivative        0.02569       -2.00416        1.48579   \n",
              "53  ZachA_15_Weeds_derivative        0.08321       -1.35619        0.75187   \n",
              "54  ZachA_14_Weeds_derivative       -0.09176       -0.82371       -0.11610   \n",
              "55  ZachA_13_Weeds_derivative       -0.00010       -1.17564        0.48237   \n",
              "56  JanD_S10_Weeds_derivative       -0.16037       -0.77824       -0.10602   \n",
              "57   JanD_S9_Weeds_derivative       -0.20803       -1.18310        0.10094   \n",
              "58  ZachA_12_Wheat_derivative       -0.18920       -1.28749       -0.23878   \n",
              "60      KB_5_Wheat_derivative       -0.26773       -1.34868       -0.64563   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "17       -0.63768        1.22855        0.29335        2  \n",
              "18       -0.04205        0.75973        0.18793        2  \n",
              "19        0.63363        0.82836       -0.11966        2  \n",
              "20       -0.07938        0.48653        0.12025        2  \n",
              "21       -0.50372        0.20397        0.32225        2  \n",
              "22       -0.44106        0.52328        0.27347        2  \n",
              "23       -0.11656        0.76114       -0.35351        2  \n",
              "50        0.77980        0.03040        0.55147        2  \n",
              "52        1.74496        0.61252        1.07818        2  \n",
              "53       -0.53835        1.63487        0.06652        2  \n",
              "54       -0.74022        0.05172       -0.03655        2  \n",
              "55        0.00453        0.44824       -0.02667        2  \n",
              "56        0.06051        0.77434        0.44745        2  \n",
              "57        0.00556        1.22714        0.34596        2  \n",
              "58        0.01147        0.45827        0.27256        2  \n",
              "60       -0.80697        0.61665        0.38074        2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cfec8e8b-3a15-4350-b015-1a8ee0417b37\" 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>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>JB_Grass_Filt_derivative</td>\n",
              "      <td>-0.24803</td>\n",
              "      <td>-1.43721</td>\n",
              "      <td>-0.31468</td>\n",
              "      <td>-0.63768</td>\n",
              "      <td>1.22855</td>\n",
              "      <td>0.29335</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>SK_5_Grass_derivative</td>\n",
              "      <td>-0.20154</td>\n",
              "      <td>-1.63808</td>\n",
              "      <td>-0.08945</td>\n",
              "      <td>-0.04205</td>\n",
              "      <td>0.75973</td>\n",
              "      <td>0.18793</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>SK_4_Grass_derivative</td>\n",
              "      <td>-0.10447</td>\n",
              "      <td>-1.75455</td>\n",
              "      <td>0.28321</td>\n",
              "      <td>0.63363</td>\n",
              "      <td>0.82836</td>\n",
              "      <td>-0.11966</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>SK_3_Grass_derivative</td>\n",
              "      <td>-0.17191</td>\n",
              "      <td>-1.62548</td>\n",
              "      <td>-0.25792</td>\n",
              "      <td>-0.07938</td>\n",
              "      <td>0.48653</td>\n",
              "      <td>0.12025</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>SK_2_Grass_derivative</td>\n",
              "      <td>-0.28412</td>\n",
              "      <td>-1.36299</td>\n",
              "      <td>-0.57340</td>\n",
              "      <td>-0.50372</td>\n",
              "      <td>0.20397</td>\n",
              "      <td>0.32225</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>SK_1_Grass_derivative</td>\n",
              "      <td>-0.26351</td>\n",
              "      <td>-1.43892</td>\n",
              "      <td>-0.41211</td>\n",
              "      <td>-0.44106</td>\n",
              "      <td>0.52328</td>\n",
              "      <td>0.27347</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>ZachA_12_Road_derivative</td>\n",
              "      <td>-0.15460</td>\n",
              "      <td>-1.26814</td>\n",
              "      <td>-0.35687</td>\n",
              "      <td>-0.11656</td>\n",
              "      <td>0.76114</td>\n",
              "      <td>-0.35351</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>JanD_S6_Weed_derivative</td>\n",
              "      <td>0.04317</td>\n",
              "      <td>-1.23126</td>\n",
              "      <td>0.60679</td>\n",
              "      <td>0.77980</td>\n",
              "      <td>0.03040</td>\n",
              "      <td>0.55147</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>JanD_S6_Weed_3_derivative</td>\n",
              "      <td>0.02569</td>\n",
              "      <td>-2.00416</td>\n",
              "      <td>1.48579</td>\n",
              "      <td>1.74496</td>\n",
              "      <td>0.61252</td>\n",
              "      <td>1.07818</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>ZachA_15_Weeds_derivative</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>-1.35619</td>\n",
              "      <td>0.75187</td>\n",
              "      <td>-0.53835</td>\n",
              "      <td>1.63487</td>\n",
              "      <td>0.06652</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>ZachA_14_Weeds_derivative</td>\n",
              "      <td>-0.09176</td>\n",
              "      <td>-0.82371</td>\n",
              "      <td>-0.11610</td>\n",
              "      <td>-0.74022</td>\n",
              "      <td>0.05172</td>\n",
              "      <td>-0.03655</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>ZachA_13_Weeds_derivative</td>\n",
              "      <td>-0.00010</td>\n",
              "      <td>-1.17564</td>\n",
              "      <td>0.48237</td>\n",
              "      <td>0.00453</td>\n",
              "      <td>0.44824</td>\n",
              "      <td>-0.02667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>JanD_S10_Weeds_derivative</td>\n",
              "      <td>-0.16037</td>\n",
              "      <td>-0.77824</td>\n",
              "      <td>-0.10602</td>\n",
              "      <td>0.06051</td>\n",
              "      <td>0.77434</td>\n",
              "      <td>0.44745</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>JanD_S9_Weeds_derivative</td>\n",
              "      <td>-0.20803</td>\n",
              "      <td>-1.18310</td>\n",
              "      <td>0.10094</td>\n",
              "      <td>0.00556</td>\n",
              "      <td>1.22714</td>\n",
              "      <td>0.34596</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>ZachA_12_Wheat_derivative</td>\n",
              "      <td>-0.18920</td>\n",
              "      <td>-1.28749</td>\n",
              "      <td>-0.23878</td>\n",
              "      <td>0.01147</td>\n",
              "      <td>0.45827</td>\n",
              "      <td>0.27256</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>KB_5_Wheat_derivative</td>\n",
              "      <td>-0.26773</td>\n",
              "      <td>-1.34868</td>\n",
              "      <td>-0.64563</td>\n",
              "      <td>-0.80697</td>\n",
              "      <td>0.61665</td>\n",
              "      <td>0.38074</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cfec8e8b-3a15-4350-b015-1a8ee0417b37 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cfec8e8b-3a15-4350-b015-1a8ee0417b37');\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",
              "\n",
              "\n",
              "<div id=\"df-2215fda5-0db2-4608-9afa-52da5ecb1fb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2215fda5-0db2-4608-9afa-52da5ecb1fb8')\"\n",
              "            title=\"Suggest charts.\"\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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              "    <g>\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2215fda5-0db2-4608-9afa-52da5ecb1fb8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Cluster_4 = components[components['cluster']==3]\n",
        "Cluster_4"
      ],
      "metadata": {
        "id": "acxzsvtomn_H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "e0e42fa3-c692-4690-dc6d-ecc163c82ab8"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "4       JB_4_Corn_Res_derivative       -0.36003       -0.36478       -1.54492   \n",
              "29      ZachA_11_Soil_derivative       -1.10787        0.18996       -0.20594   \n",
              "46          JB_3_Soil_derivative       -0.30734       -0.32592       -1.19419   \n",
              "61  KB_5_Drying_Wheat_derivative       -0.14781       -0.75104       -0.69937   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "4        -1.79321       -1.13306        0.53164        3  \n",
              "29       -0.66953       -3.02616       -0.42628        3  \n",
              "46       -1.84323       -1.14022        0.39229        3  \n",
              "61        0.26585       -2.86606        0.51895        3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7793d276-8ea3-49c7-bb6e-cc273a37da47\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JB_4_Corn_Res_derivative</td>\n",
              "      <td>-0.36003</td>\n",
              "      <td>-0.36478</td>\n",
              "      <td>-1.54492</td>\n",
              "      <td>-1.79321</td>\n",
              "      <td>-1.13306</td>\n",
              "      <td>0.53164</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>ZachA_11_Soil_derivative</td>\n",
              "      <td>-1.10787</td>\n",
              "      <td>0.18996</td>\n",
              "      <td>-0.20594</td>\n",
              "      <td>-0.66953</td>\n",
              "      <td>-3.02616</td>\n",
              "      <td>-0.42628</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>JB_3_Soil_derivative</td>\n",
              "      <td>-0.30734</td>\n",
              "      <td>-0.32592</td>\n",
              "      <td>-1.19419</td>\n",
              "      <td>-1.84323</td>\n",
              "      <td>-1.14022</td>\n",
              "      <td>0.39229</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>KB_5_Drying_Wheat_derivative</td>\n",
              "      <td>-0.14781</td>\n",
              "      <td>-0.75104</td>\n",
              "      <td>-0.69937</td>\n",
              "      <td>0.26585</td>\n",
              "      <td>-2.86606</td>\n",
              "      <td>0.51895</td>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .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-7793d276-8ea3-49c7-bb6e-cc273a37da47');\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",
              "\n",
              "\n",
              "<div id=\"df-21138aeb-ef58-43c1-b6bd-7fad97a56597\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21138aeb-ef58-43c1-b6bd-7fad97a56597')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-21138aeb-ef58-43c1-b6bd-7fad97a56597 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Cluster_5 = components[components['cluster']==4]\n",
        "Cluster_5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "MuuU8NCXnAoO",
        "outputId": "4313a455-6676-4937-a3b8-3647135d8ec2"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "35       SK_5_Soil_derivative       -0.07686       -0.31692       -0.33524   \n",
              "36       SK_4_Soil_derivative       -0.02377        0.09830       -0.24788   \n",
              "37       SK_3_Soil_derivative        0.15137        0.19895       -0.04729   \n",
              "38       SK_2_Soil_derivative       -0.04729       -0.53887       -0.47901   \n",
              "51  JanD_S6_Weed_2_derivative        0.28172       -1.57183        1.91510   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "35        1.19890       -0.29101       -0.82981        4  \n",
              "36        1.81853       -1.07998       -1.15046        4  \n",
              "37        3.72846       -1.37945       -1.74569        4  \n",
              "38        1.49406       -0.91579       -1.06076        4  \n",
              "51        2.72241       -1.20414        0.79381        4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5653a70e-db5e-4ba7-bb2e-5ffc81332fcb\" 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>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>SK_5_Soil_derivative</td>\n",
              "      <td>-0.07686</td>\n",
              "      <td>-0.31692</td>\n",
              "      <td>-0.33524</td>\n",
              "      <td>1.19890</td>\n",
              "      <td>-0.29101</td>\n",
              "      <td>-0.82981</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>SK_4_Soil_derivative</td>\n",
              "      <td>-0.02377</td>\n",
              "      <td>0.09830</td>\n",
              "      <td>-0.24788</td>\n",
              "      <td>1.81853</td>\n",
              "      <td>-1.07998</td>\n",
              "      <td>-1.15046</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>SK_3_Soil_derivative</td>\n",
              "      <td>0.15137</td>\n",
              "      <td>0.19895</td>\n",
              "      <td>-0.04729</td>\n",
              "      <td>3.72846</td>\n",
              "      <td>-1.37945</td>\n",
              "      <td>-1.74569</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>SK_2_Soil_derivative</td>\n",
              "      <td>-0.04729</td>\n",
              "      <td>-0.53887</td>\n",
              "      <td>-0.47901</td>\n",
              "      <td>1.49406</td>\n",
              "      <td>-0.91579</td>\n",
              "      <td>-1.06076</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>JanD_S6_Weed_2_derivative</td>\n",
              "      <td>0.28172</td>\n",
              "      <td>-1.57183</td>\n",
              "      <td>1.91510</td>\n",
              "      <td>2.72241</td>\n",
              "      <td>-1.20414</td>\n",
              "      <td>0.79381</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5653a70e-db5e-4ba7-bb2e-5ffc81332fcb')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5653a70e-db5e-4ba7-bb2e-5ffc81332fcb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5653a70e-db5e-4ba7-bb2e-5ffc81332fcb');\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",
              "\n",
              "\n",
              "<div id=\"df-eec7b6bc-d5ac-4b06-8adb-b7644217e117\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eec7b6bc-d5ac-4b06-8adb-b7644217e117')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eec7b6bc-d5ac-4b06-8adb-b7644217e117 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_6 = components[components['cluster']==5]\n",
        "Cluster_6\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "jpxx5yM2pqnD",
        "outputId": "67bc9409-d5e0-4abd-bfc3-1abe171c7d47"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "0       KB_5_Corn_Res_derivative        0.58711        1.45673       -0.78512   \n",
              "1       KB_3_Corn_Res_derivative        0.47937        1.42302       -0.83547   \n",
              "2       KB_1_Corn_Res_derivative        0.51947        1.47856       -0.84628   \n",
              "7       JB_1_Corn_Res_derivative        0.71224        1.24818        0.79963   \n",
              "8    JanD_S9_Corn_Res_derivative        0.26717        0.69057       -0.86472   \n",
              "9    JanD_S8_Corn_Res_derivative        0.79154        1.46062       -0.46650   \n",
              "10  JanD_S6_Corn_Rewd_derivative        0.69243        1.08887       -0.43473   \n",
              "12   LynnW_04_CornRes_derivative        0.58994        1.16021       -0.71404   \n",
              "13   LynnW_03_CornRes_derivative        0.39453        0.93831       -0.94355   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "0         0.95528        0.85001        1.35848        5  \n",
              "1         1.16594        0.95517        0.77655        5  \n",
              "2         1.32242        1.13929        0.74541        5  \n",
              "7         0.21658        0.96426        0.23944        5  \n",
              "8         0.33886        0.03211        0.98996        5  \n",
              "9         0.25406        0.10170        2.98642        5  \n",
              "10        0.43256        0.31608        2.43502        5  \n",
              "12        0.52023        1.66768       -1.04353        5  \n",
              "13        0.02916        0.77398        0.59516        5  "
            ],
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              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
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              "      <th>6VPCA Score 4</th>\n",
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              "      <th>cluster</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KB_5_Corn_Res_derivative</td>\n",
              "      <td>0.58711</td>\n",
              "      <td>1.45673</td>\n",
              "      <td>-0.78512</td>\n",
              "      <td>0.95528</td>\n",
              "      <td>0.85001</td>\n",
              "      <td>1.35848</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KB_3_Corn_Res_derivative</td>\n",
              "      <td>0.47937</td>\n",
              "      <td>1.42302</td>\n",
              "      <td>-0.83547</td>\n",
              "      <td>1.16594</td>\n",
              "      <td>0.95517</td>\n",
              "      <td>0.77655</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KB_1_Corn_Res_derivative</td>\n",
              "      <td>0.51947</td>\n",
              "      <td>1.47856</td>\n",
              "      <td>-0.84628</td>\n",
              "      <td>1.32242</td>\n",
              "      <td>1.13929</td>\n",
              "      <td>0.74541</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>JB_1_Corn_Res_derivative</td>\n",
              "      <td>0.71224</td>\n",
              "      <td>1.24818</td>\n",
              "      <td>0.79963</td>\n",
              "      <td>0.21658</td>\n",
              "      <td>0.96426</td>\n",
              "      <td>0.23944</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>JanD_S9_Corn_Res_derivative</td>\n",
              "      <td>0.26717</td>\n",
              "      <td>0.69057</td>\n",
              "      <td>-0.86472</td>\n",
              "      <td>0.33886</td>\n",
              "      <td>0.03211</td>\n",
              "      <td>0.98996</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>JanD_S8_Corn_Res_derivative</td>\n",
              "      <td>0.79154</td>\n",
              "      <td>1.46062</td>\n",
              "      <td>-0.46650</td>\n",
              "      <td>0.25406</td>\n",
              "      <td>0.10170</td>\n",
              "      <td>2.98642</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>JanD_S6_Corn_Rewd_derivative</td>\n",
              "      <td>0.69243</td>\n",
              "      <td>1.08887</td>\n",
              "      <td>-0.43473</td>\n",
              "      <td>0.43256</td>\n",
              "      <td>0.31608</td>\n",
              "      <td>2.43502</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LynnW_04_CornRes_derivative</td>\n",
              "      <td>0.58994</td>\n",
              "      <td>1.16021</td>\n",
              "      <td>-0.71404</td>\n",
              "      <td>0.52023</td>\n",
              "      <td>1.66768</td>\n",
              "      <td>-1.04353</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LynnW_03_CornRes_derivative</td>\n",
              "      <td>0.39453</td>\n",
              "      <td>0.93831</td>\n",
              "      <td>-0.94355</td>\n",
              "      <td>0.02916</td>\n",
              "      <td>0.77398</td>\n",
              "      <td>0.59516</td>\n",
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          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "KBxrUEi-0WuC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_7 = components[components['cluster']==6]\n",
        "Cluster_7\n"
      ],
      "metadata": {
        "id": "75IqpC2N0TOV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "428cc2a1-6274-4a7d-bf82-40104ace163b"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "15  LynnW_01_CornRes_derivative        0.79962        1.08088        0.03101   \n",
              "26     ZachA_15_Soil_derivative        0.30610        0.43203        1.04386   \n",
              "27     ZachA_14_Soil_derivative        0.21687        0.50350        1.05752   \n",
              "28     ZachA_13_Soil_derivative        0.02198        0.07955        0.29294   \n",
              "34     LynnW_01_Soil_derivative        0.45344        0.49939        0.77485   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "15        0.04077        1.19361       -1.85869        6  \n",
              "26       -1.45887        0.59110       -1.13809        6  \n",
              "27       -1.10075        0.57432       -1.13913        6  \n",
              "28       -1.37950        0.17003       -1.26736        6  \n",
              "34        0.17617       -0.80337       -2.83282        6  "
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              "\n",
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              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LynnW_01_CornRes_derivative</td>\n",
              "      <td>0.79962</td>\n",
              "      <td>1.08088</td>\n",
              "      <td>0.03101</td>\n",
              "      <td>0.04077</td>\n",
              "      <td>1.19361</td>\n",
              "      <td>-1.85869</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>ZachA_15_Soil_derivative</td>\n",
              "      <td>0.30610</td>\n",
              "      <td>0.43203</td>\n",
              "      <td>1.04386</td>\n",
              "      <td>-1.45887</td>\n",
              "      <td>0.59110</td>\n",
              "      <td>-1.13809</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>ZachA_14_Soil_derivative</td>\n",
              "      <td>0.21687</td>\n",
              "      <td>0.50350</td>\n",
              "      <td>1.05752</td>\n",
              "      <td>-1.10075</td>\n",
              "      <td>0.57432</td>\n",
              "      <td>-1.13913</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>ZachA_13_Soil_derivative</td>\n",
              "      <td>0.02198</td>\n",
              "      <td>0.07955</td>\n",
              "      <td>0.29294</td>\n",
              "      <td>-1.37950</td>\n",
              "      <td>0.17003</td>\n",
              "      <td>-1.26736</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>LynnW_01_Soil_derivative</td>\n",
              "      <td>0.45344</td>\n",
              "      <td>0.49939</td>\n",
              "      <td>0.77485</td>\n",
              "      <td>0.17617</td>\n",
              "      <td>-0.80337</td>\n",
              "      <td>-2.83282</td>\n",
              "      <td>6</td>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e3f95277-2ce4-490c-81ee-0c834e181e2e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e3f95277-2ce4-490c-81ee-0c834e181e2e');\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",
              "\n",
              "\n",
              "<div id=\"df-9a9d0020-4d70-40d4-b5cb-24d26d5681aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a9d0020-4d70-40d4-b5cb-24d26d5681aa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9a9d0020-4d70-40d4-b5cb-24d26d5681aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_8 = components[components['cluster']==7]\n",
        "Cluster_8\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "sLeC_jvKkaCf",
        "outputId": "7ac5f311-6e09-4611-fb44-8dccbd4c02b8"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Sample, 6VPCA Score 1, 6VPCA Score 2, 6VPCA Score 3, 6VPCA Score 4, 6VPCA Score 5, 6VPCA Score 6, cluster]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f55c30a-416e-4b9d-869a-8ab6bf72af37\" 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",
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              "    }\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>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f55c30a-416e-4b9d-869a-8ab6bf72af37')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1f55c30a-416e-4b9d-869a-8ab6bf72af37 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1f55c30a-416e-4b9d-869a-8ab6bf72af37');\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",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_9 = components[components['cluster']==8]\n",
        "Cluster_9\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "hF693X-jkhB0",
        "outputId": "0be84b2f-a79a-4e16-fa15-73880cb4938d"
      },
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "17   JB_Grass_Filt_derivative       -0.24803       -1.43721       -0.31468   \n",
              "18      SK_5_Grass_derivative       -0.20154       -1.63808       -0.08945   \n",
              "19      SK_4_Grass_derivative       -0.10447       -1.75455        0.28321   \n",
              "20      SK_3_Grass_derivative       -0.17191       -1.62548       -0.25792   \n",
              "21      SK_2_Grass_derivative       -0.28412       -1.36299       -0.57340   \n",
              "22      SK_1_Grass_derivative       -0.26351       -1.43892       -0.41211   \n",
              "23   ZachA_12_Road_derivative       -0.15460       -1.26814       -0.35687   \n",
              "53  ZachA_15_Weeds_derivative        0.08321       -1.35619        0.75187   \n",
              "54  ZachA_14_Weeds_derivative       -0.09176       -0.82371       -0.11610   \n",
              "55  ZachA_13_Weeds_derivative       -0.00010       -1.17564        0.48237   \n",
              "56  JanD_S10_Weeds_derivative       -0.16037       -0.77824       -0.10602   \n",
              "57   JanD_S9_Weeds_derivative       -0.20803       -1.18310        0.10094   \n",
              "58  ZachA_12_Wheat_derivative       -0.18920       -1.28749       -0.23878   \n",
              "60      KB_5_Wheat_derivative       -0.26773       -1.34868       -0.64563   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "17       -0.63768        1.22855        0.29335        8  \n",
              "18       -0.04205        0.75973        0.18793        8  \n",
              "19        0.63363        0.82836       -0.11966        8  \n",
              "20       -0.07938        0.48653        0.12025        8  \n",
              "21       -0.50372        0.20397        0.32225        8  \n",
              "22       -0.44106        0.52328        0.27347        8  \n",
              "23       -0.11656        0.76114       -0.35351        8  \n",
              "53       -0.53835        1.63487        0.06652        8  \n",
              "54       -0.74022        0.05172       -0.03655        8  \n",
              "55        0.00453        0.44824       -0.02667        8  \n",
              "56        0.06051        0.77434        0.44745        8  \n",
              "57        0.00556        1.22714        0.34596        8  \n",
              "58        0.01147        0.45827        0.27256        8  \n",
              "60       -0.80697        0.61665        0.38074        8  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b5ba8fe-9ee9-4467-b9ed-d9f2673e311d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\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>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>JB_Grass_Filt_derivative</td>\n",
              "      <td>-0.24803</td>\n",
              "      <td>-1.43721</td>\n",
              "      <td>-0.31468</td>\n",
              "      <td>-0.63768</td>\n",
              "      <td>1.22855</td>\n",
              "      <td>0.29335</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>SK_5_Grass_derivative</td>\n",
              "      <td>-0.20154</td>\n",
              "      <td>-1.63808</td>\n",
              "      <td>-0.08945</td>\n",
              "      <td>-0.04205</td>\n",
              "      <td>0.75973</td>\n",
              "      <td>0.18793</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>SK_4_Grass_derivative</td>\n",
              "      <td>-0.10447</td>\n",
              "      <td>-1.75455</td>\n",
              "      <td>0.28321</td>\n",
              "      <td>0.63363</td>\n",
              "      <td>0.82836</td>\n",
              "      <td>-0.11966</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>SK_3_Grass_derivative</td>\n",
              "      <td>-0.17191</td>\n",
              "      <td>-1.62548</td>\n",
              "      <td>-0.25792</td>\n",
              "      <td>-0.07938</td>\n",
              "      <td>0.48653</td>\n",
              "      <td>0.12025</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>SK_2_Grass_derivative</td>\n",
              "      <td>-0.28412</td>\n",
              "      <td>-1.36299</td>\n",
              "      <td>-0.57340</td>\n",
              "      <td>-0.50372</td>\n",
              "      <td>0.20397</td>\n",
              "      <td>0.32225</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>SK_1_Grass_derivative</td>\n",
              "      <td>-0.26351</td>\n",
              "      <td>-1.43892</td>\n",
              "      <td>-0.41211</td>\n",
              "      <td>-0.44106</td>\n",
              "      <td>0.52328</td>\n",
              "      <td>0.27347</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>ZachA_12_Road_derivative</td>\n",
              "      <td>-0.15460</td>\n",
              "      <td>-1.26814</td>\n",
              "      <td>-0.35687</td>\n",
              "      <td>-0.11656</td>\n",
              "      <td>0.76114</td>\n",
              "      <td>-0.35351</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>ZachA_15_Weeds_derivative</td>\n",
              "      <td>0.08321</td>\n",
              "      <td>-1.35619</td>\n",
              "      <td>0.75187</td>\n",
              "      <td>-0.53835</td>\n",
              "      <td>1.63487</td>\n",
              "      <td>0.06652</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>ZachA_14_Weeds_derivative</td>\n",
              "      <td>-0.09176</td>\n",
              "      <td>-0.82371</td>\n",
              "      <td>-0.11610</td>\n",
              "      <td>-0.74022</td>\n",
              "      <td>0.05172</td>\n",
              "      <td>-0.03655</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>ZachA_13_Weeds_derivative</td>\n",
              "      <td>-0.00010</td>\n",
              "      <td>-1.17564</td>\n",
              "      <td>0.48237</td>\n",
              "      <td>0.00453</td>\n",
              "      <td>0.44824</td>\n",
              "      <td>-0.02667</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>JanD_S10_Weeds_derivative</td>\n",
              "      <td>-0.16037</td>\n",
              "      <td>-0.77824</td>\n",
              "      <td>-0.10602</td>\n",
              "      <td>0.06051</td>\n",
              "      <td>0.77434</td>\n",
              "      <td>0.44745</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>JanD_S9_Weeds_derivative</td>\n",
              "      <td>-0.20803</td>\n",
              "      <td>-1.18310</td>\n",
              "      <td>0.10094</td>\n",
              "      <td>0.00556</td>\n",
              "      <td>1.22714</td>\n",
              "      <td>0.34596</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>ZachA_12_Wheat_derivative</td>\n",
              "      <td>-0.18920</td>\n",
              "      <td>-1.28749</td>\n",
              "      <td>-0.23878</td>\n",
              "      <td>0.01147</td>\n",
              "      <td>0.45827</td>\n",
              "      <td>0.27256</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>KB_5_Wheat_derivative</td>\n",
              "      <td>-0.26773</td>\n",
              "      <td>-1.34868</td>\n",
              "      <td>-0.64563</td>\n",
              "      <td>-0.80697</td>\n",
              "      <td>0.61665</td>\n",
              "      <td>0.38074</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b5ba8fe-9ee9-4467-b9ed-d9f2673e311d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b5ba8fe-9ee9-4467-b9ed-d9f2673e311d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7b5ba8fe-9ee9-4467-b9ed-d9f2673e311d');\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",
              "\n",
              "\n",
              "<div id=\"df-6308e465-d2b0-4c49-8d78-98291ca8c112\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6308e465-d2b0-4c49-8d78-98291ca8c112')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6308e465-d2b0-4c49-8d78-98291ca8c112 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cluster_10 = components[components['cluster']==9]\n",
        "Cluster_10\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_DefXQwh3Kes",
        "outputId": "cab301a1-5ef9-46c6-db08-2e700ed9c814"
      },
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sample  6VPCA Score 1  6VPCA Score 2  6VPCA Score 3  \\\n",
              "4       JB_4_Corn_Res_derivative       -0.36003       -0.36478       -1.54492   \n",
              "46          JB_3_Soil_derivative       -0.30734       -0.32592       -1.19419   \n",
              "61  KB_5_Drying_Wheat_derivative       -0.14781       -0.75104       -0.69937   \n",
              "\n",
              "    6VPCA Score 4  6VPCA Score 5  6VPCA Score 6  cluster  \n",
              "4        -1.79321       -1.13306        0.53164        9  \n",
              "46       -1.84323       -1.14022        0.39229        9  \n",
              "61        0.26585       -2.86606        0.51895        9  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31410510-a1a5-40be-ad9d-d6b60082b62d\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>6VPCA Score 1</th>\n",
              "      <th>6VPCA Score 2</th>\n",
              "      <th>6VPCA Score 3</th>\n",
              "      <th>6VPCA Score 4</th>\n",
              "      <th>6VPCA Score 5</th>\n",
              "      <th>6VPCA Score 6</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JB_4_Corn_Res_derivative</td>\n",
              "      <td>-0.36003</td>\n",
              "      <td>-0.36478</td>\n",
              "      <td>-1.54492</td>\n",
              "      <td>-1.79321</td>\n",
              "      <td>-1.13306</td>\n",
              "      <td>0.53164</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>JB_3_Soil_derivative</td>\n",
              "      <td>-0.30734</td>\n",
              "      <td>-0.32592</td>\n",
              "      <td>-1.19419</td>\n",
              "      <td>-1.84323</td>\n",
              "      <td>-1.14022</td>\n",
              "      <td>0.39229</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>KB_5_Drying_Wheat_derivative</td>\n",
              "      <td>-0.14781</td>\n",
              "      <td>-0.75104</td>\n",
              "      <td>-0.69937</td>\n",
              "      <td>0.26585</td>\n",
              "      <td>-2.86606</td>\n",
              "      <td>0.51895</td>\n",
              "      <td>9</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "\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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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-31410510-a1a5-40be-ad9d-d6b60082b62d');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7378dddd-f8c1-40a4-95dc-02b9183998b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***SUMMARY***\n",
        "The model does a good job at clustering the VPCA of the field data into its original field cover. Hoever, there are a lot of overlaps in the clusters.\n"
      ],
      "metadata": {
        "id": "utqBhvhJb-dE"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}