k_means_Clustering_of_VPCA_Spectra (1).ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"Xe6aWXQaEEaw"},"source":["#K-Means Model for Clustering the Soil Cover Classes Based on VPCA Dimension Reduction\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"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MmkFyV1MFEtl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695793900356,"user_tz":240,"elapsed":23121,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"673c29e9-2121-4623-c454-892428d66411"},"outputs":[{"output_type":"stream","name":"stdout","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":null,"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","component = component.drop([59])\n"]},{"cell_type":"code","source":["component"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"zSKGELIZhSUS","executionInfo":{"status":"ok","timestamp":1695794578921,"user_tz":240,"elapsed":145,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"de307725-ad8b-4779-f2d0-2cfb8d9a3e30"},"execution_count":null,"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]"],"text/html":["\n","  <div id=\"df-4fa8173f-9767-4e1e-abcd-ce1c334565f2\" 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","    </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","      <td>...</td>\n","      <td>...</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","    </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","    </tr>\n","  </tbody>\n","</table>\n","<p>61 rows × 7 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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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, init='k-means++', n_init='auto', max_iter=1000, tol=0.0001, algorithm='lloyd', 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":null,"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","executionInfo":{"status":"ok","timestamp":1695794595215,"user_tz":240,"elapsed":135,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"0cb7647e-d255-42e6-be4a-b29d4fea6edd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[282.56739689383534,\n"," 244.93575569423376,\n"," 203.12752668279822,\n"," 176.18587694349702,\n"," 137.80290996094737,\n"," 119.65264001591488,\n"," 110.5411051108496,\n"," 96.8296246035528,\n"," 88.6857121234481,\n"," 80.81302909050571]"]},"metadata":{},"execution_count":33}]},{"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","executionInfo":{"status":"ok","timestamp":1695794600983,"user_tz":240,"elapsed":339,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"674a0952-d19d-4a40-b236-98e5b7e74ccb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"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, init='k-means++', n_init='auto', max_iter=1000, tol=0.0001, algorithm='lloyd', 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","executionInfo":{"status":"ok","timestamp":1695794623277,"user_tz":240,"elapsed":795,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"9d81cf12-c66a-48db-bef5-26e942b9f86f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.24064683432964232,\n"," 0.2226955785128655,\n"," 0.26219278170589777,\n"," 0.2458947307467138,\n"," 0.302957009736,\n"," 0.29479244416712586,\n"," 0.31798868098225314,\n"," 0.32614142907516724,\n"," 0.3049511047833141,\n"," 0.32109396107791505]"]},"metadata":{},"execution_count":35}]},{"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","executionInfo":{"status":"ok","timestamp":1695794631885,"user_tz":240,"elapsed":731,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"33625888-4de9-440d-9ff6-a92a41171871"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"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=6, init='k-means++', n_init='auto', max_iter=1000, tol=10000, algorithm='lloyd', random_state=42)\n","model.fit(X_scaled)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"y7ws9i-KGXMH","executionInfo":{"status":"ok","timestamp":1695794961358,"user_tz":240,"elapsed":169,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"39ea71d3-0785-48ba-b3ce-2a5758ac2cb2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KMeans(max_iter=1000, n_clusters=6, n_init='auto', random_state=42, tol=10000)"],"text/html":["<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=1000, n_clusters=6, n_init=&#x27;auto&#x27;, random_state=42, tol=10000)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(max_iter=1000, n_clusters=6, n_init=&#x27;auto&#x27;, random_state=42, tol=10000)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":62}]},{"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","executionInfo":{"status":"ok","timestamp":1695794963262,"user_tz":240,"elapsed":135,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"14fc202e-9af1-4a82-e18d-69dced36b3d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Unique labels:  [0 1 2 3 4 5]\n"]}]},{"cell_type":"markdown","source":["*** Now, Lets add a new column called clusters to our derivative reflectance and get the sizes of each cluster***"],"metadata":{"id":"yvDZDE1AMtal"}},{"cell_type":"code","source":["\n","component['cluster'] = model.labels_"],"metadata":{"id":"Fkxc9hlaGx4b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[" #We want to verify the size of the cluster`.\n","\n","component.groupby(by = ['cluster']).size()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9lsXBJ-bHscq","executionInfo":{"status":"ok","timestamp":1695794971663,"user_tz":240,"elapsed":239,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"2ebedb14-7a43-4556-b81b-c6f5a52d8652"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["cluster\n","0     7\n","1     7\n","2     9\n","3    13\n","4     9\n","5    16\n","dtype: int64"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["#We want to plot the sizes of the cluster centers\n","component.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","executionInfo":{"status":"ok","timestamp":1695794980800,"user_tz":240,"elapsed":624,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"4cb3d7c6-9f10-410e-c262-e1482ee0333e"},"execution_count":null,"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(component[component['cluster'] == i]['6VPCA Score 1' ],component[component['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","executionInfo":{"status":"ok","timestamp":1695794987627,"user_tz":240,"elapsed":795,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"5fdd92de-e667-4e69-8d89-b62d101c233f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["Cluster_1 = components[components['cluster']==0]\n","Cluster_1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"8gumxa31Sf3i","executionInfo":{"status":"ok","timestamp":1695654966561,"user_tz":240,"elapsed":146,"user":{"displayName":"Mohammed Braimah","userId":"00479211081335599534"}},"outputId":"2ea5056f-e39f-4717-915d-16eefce869bf"},"execution_count":null,"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-d684708e-c670-49e7-8c2a-ed9aa5f88af6\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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<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","    <div class=\"colab-df-buttons\">\n","\n","  <div 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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","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<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","    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-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","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfec8e8b-3a15-4350-b015-1a8ee0417b37')\"\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 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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","     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-2215fda5-0db2-4608-9afa-52da5ecb1fb8 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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   -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","<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>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","      <td>3</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-7793d276-8ea3-49c7-bb6e-cc273a37da47')\"\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","  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   -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 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 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  "],"text/html":["\n","  <div id=\"df-35f6674a-1b85-4f09-a48b-8a97c3968204\" 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>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","      <td>5</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-35f6674a-1b85-4f09-a48b-8a97c3968204')\"\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 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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  "],"text/html":["\n","  <div id=\"df-e3f95277-2ce4-490c-81ee-0c834e181e2e\" 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\" 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<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","    </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\" 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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","executionInfo":{"status":"ok","timestamp":1695000903863,"user_tz":240,"elapsed":162,"user":{"displayName":"Mohammed 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\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","    .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>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","            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","    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