ondo-daily-data-wf.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8516947,"sourceType":"datasetVersion","datasetId":5084967},{"sourceId":9042268,"sourceType":"datasetVersion","datasetId":5451388}],"dockerImageVersionId":30746,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport joblib","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:05:28.333540Z","iopub.execute_input":"2024-07-28T20:05:28.333996Z","iopub.status.idle":"2024-07-28T20:05:31.376990Z","shell.execute_reply.started":"2024-07-28T20:05:28.333961Z","shell.execute_reply":"2024-07-28T20:05:31.375405Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import warnings","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:05:35.937384Z","iopub.execute_input":"2024-07-28T20:05:35.938384Z","iopub.status.idle":"2024-07-28T20:05:35.943814Z","shell.execute_reply.started":"2024-07-28T20:05:35.938348Z","shell.execute_reply":"2024-07-28T20:05:35.942361Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"#supress warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:05:38.370160Z","iopub.execute_input":"2024-07-28T20:05:38.371563Z","iopub.status.idle":"2024-07-28T20:05:38.377599Z","shell.execute_reply.started":"2024-07-28T20:05:38.371508Z","shell.execute_reply":"2024-07-28T20:05:38.376117Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# Load the data\nfile_path = '/kaggle/input/ondodata-dmy/Ondo_weatherdataDMY.csv'\ndata = pd.read_csv(file_path)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:09.250250Z","iopub.execute_input":"2024-07-28T20:06:09.250719Z","iopub.status.idle":"2024-07-28T20:06:09.304602Z","shell.execute_reply.started":"2024-07-28T20:06:09.250685Z","shell.execute_reply":"2024-07-28T20:06:09.303340Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# Display the first few rows of the dataframe\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:12.185209Z","iopub.execute_input":"2024-07-28T20:06:12.186202Z","iopub.status.idle":"2024-07-28T20:06:12.217046Z","shell.execute_reply.started":"2024-07-28T20:06:12.186163Z","shell.execute_reply":"2024-07-28T20:06:12.215839Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"         Date  Relative_Humidity  Max_Temp  Min_Temp  Precipitation  \\\n0  1982-01-01              79.75     31.35     21.21           0.10   \n1  1982-01-02              77.31     30.94     20.71           0.26   \n2  1982-01-03              83.69     30.48     22.44           1.33   \n3  1982-01-04              77.31     29.77     21.28           0.95   \n4  1982-01-05              64.94     29.82     18.30           0.00   \n\n   Soil_wetness  \n0          0.72  \n1          0.72  \n2          0.72  \n3          0.72  \n4          0.71  ","text/html":"<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>Date</th>\n      <th>Relative_Humidity</th>\n      <th>Max_Temp</th>\n      <th>Min_Temp</th>\n      <th>Precipitation</th>\n      <th>Soil_wetness</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1982-01-01</td>\n      <td>79.75</td>\n      <td>31.35</td>\n      <td>21.21</td>\n      <td>0.10</td>\n      <td>0.72</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1982-01-02</td>\n      <td>77.31</td>\n      <td>30.94</td>\n      <td>20.71</td>\n      <td>0.26</td>\n      <td>0.72</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1982-01-03</td>\n      <td>83.69</td>\n      <td>30.48</td>\n      <td>22.44</td>\n      <td>1.33</td>\n      <td>0.72</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1982-01-04</td>\n      <td>77.31</td>\n      <td>29.77</td>\n      <td>21.28</td>\n      <td>0.95</td>\n      <td>0.72</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1982-01-05</td>\n      <td>64.94</td>\n      <td>29.82</td>\n      <td>18.30</td>\n      <td>0.00</td>\n      <td>0.71</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Set the 'Date' column as the index\ndata.set_index('Date', inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:23.360190Z","iopub.execute_input":"2024-07-28T20:06:23.360680Z","iopub.status.idle":"2024-07-28T20:06:23.371330Z","shell.execute_reply.started":"2024-07-28T20:06:23.360644Z","shell.execute_reply":"2024-07-28T20:06:23.369807Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Keep only the 'Relative_Humidity' column\ndata_rh = data[['Relative_Humidity']]\ndata_max_temp = data[['Max_Temp']]\ndata_min_temp = data[['Min_Temp']]\ndata_precipitation = data[['Precipitation']]\ndata_soil_wetness = data[['Soil_wetness']]","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:27.599103Z","iopub.execute_input":"2024-07-28T20:06:27.599579Z","iopub.status.idle":"2024-07-28T20:06:27.615955Z","shell.execute_reply.started":"2024-07-28T20:06:27.599547Z","shell.execute_reply":"2024-07-28T20:06:27.614448Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Display the first few rows to confirm changes\ndata_rh.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:32.558985Z","iopub.execute_input":"2024-07-28T20:06:32.560107Z","iopub.status.idle":"2024-07-28T20:06:32.571295Z","shell.execute_reply.started":"2024-07-28T20:06:32.560072Z","shell.execute_reply":"2024-07-28T20:06:32.570137Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"            Relative_Humidity\nDate                         \n1982-01-01              79.75\n1982-01-02              77.31\n1982-01-03              83.69\n1982-01-04              77.31\n1982-01-05              64.94","text/html":"<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>Relative_Humidity</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1982-01-01</th>\n      <td>79.75</td>\n    </tr>\n    <tr>\n      <th>1982-01-02</th>\n      <td>77.31</td>\n    </tr>\n    <tr>\n      <th>1982-01-03</th>\n      <td>83.69</td>\n    </tr>\n    <tr>\n      <th>1982-01-04</th>\n      <td>77.31</td>\n    </tr>\n    <tr>\n      <th>1982-01-05</th>\n      <td>64.94</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Display the first few rows to confirm changes\ndata_rh.tail()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:36.061090Z","iopub.execute_input":"2024-07-28T20:06:36.062650Z","iopub.status.idle":"2024-07-28T20:06:36.075557Z","shell.execute_reply.started":"2024-07-28T20:06:36.062585Z","shell.execute_reply":"2024-07-28T20:06:36.074164Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"            Relative_Humidity\nDate                         \n2023-12-27              69.50\n2023-12-28              62.56\n2023-12-29              66.06\n2023-12-30              71.69\n2023-12-31              76.94","text/html":"<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>Relative_Humidity</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023-12-27</th>\n      <td>69.50</td>\n    </tr>\n    <tr>\n      <th>2023-12-28</th>\n      <td>62.56</td>\n    </tr>\n    <tr>\n      <th>2023-12-29</th>\n      <td>66.06</td>\n    </tr>\n    <tr>\n      <th>2023-12-30</th>\n      <td>71.69</td>\n    </tr>\n    <tr>\n      <th>2023-12-31</th>\n      <td>76.94</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Split the data into training and testing sets\ntrain = data_rh.loc[:'2022-12-01']\ntest = data_rh.loc['2023-01-01':]","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:39.186042Z","iopub.execute_input":"2024-07-28T20:06:39.186499Z","iopub.status.idle":"2024-07-28T20:06:39.195151Z","shell.execute_reply.started":"2024-07-28T20:06:39.186467Z","shell.execute_reply":"2024-07-28T20:06:39.193635Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# Define the SARIMA model\nmodel_rh = SARIMAX(train, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:43.228050Z","iopub.execute_input":"2024-07-28T20:06:43.229355Z","iopub.status.idle":"2024-07-28T20:06:43.269145Z","shell.execute_reply.started":"2024-07-28T20:06:43.229309Z","shell.execute_reply":"2024-07-28T20:06:43.267587Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"# Fit the model\nsarima_rh_model = model_rh.fit()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:06:45.568221Z","iopub.execute_input":"2024-07-28T20:06:45.568680Z","iopub.status.idle":"2024-07-28T20:07:52.218683Z","shell.execute_reply.started":"2024-07-28T20:06:45.568646Z","shell.execute_reply":"2024-07-28T20:07:52.217304Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"RUNNING THE L-BFGS-B CODE\n\n           * * *\n\nMachine precision = 2.220D-16\n N =            5     M =           10\n\nAt X0         0 variables are exactly at the bounds\n\nAt iterate    0    f=  2.76330D+00    |proj g|=  8.96898D-02\n","output_type":"stream"},{"name":"stderr","text":" This problem is unconstrained.\n","output_type":"stream"},{"name":"stdout","text":"\nAt iterate    5    f=  2.63598D+00    |proj g|=  2.89390D-02\n\nAt iterate   10    f=  2.61545D+00    |proj g|=  2.52190D-03\n\nAt iterate   15    f=  2.61294D+00    |proj g|=  1.74769D-03\n\nAt iterate   20    f=  2.61266D+00    |proj g|=  1.74151D-03\n\nAt iterate   25    f=  2.61264D+00    |proj g|=  2.17903D-04\n\nAt iterate   30    f=  2.61264D+00    |proj g|=  6.97151D-05\n\n           * * *\n\nTit   = total number of iterations\nTnf   = total number of function evaluations\nTnint = total number of segments explored during Cauchy searches\nSkip  = number of BFGS updates skipped\nNact  = number of active bounds at final generalized Cauchy point\nProjg = norm of the final projected gradient\nF     = final function value\n\n           * * *\n\n   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n    5     34     40      1     0     0   3.591D-05   2.613D+00\n  F =   2.6126369917513323     \n\nCONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n","output_type":"stream"}]},{"cell_type":"code","source":"# Forecast future values from 2024 to 2050\nforecast_rh = sarima_rh_model.get_forecast(steps=10288)\nforecasted_rh_values = forecast_rh.predicted_mean\nforecast_rh_index = pd.date_range(start='2024-01-01', end='2050-12-31', freq='MS')","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:03.113954Z","iopub.execute_input":"2024-07-28T21:12:03.115661Z","iopub.status.idle":"2024-07-28T21:12:03.834628Z","shell.execute_reply.started":"2024-07-28T21:12:03.115619Z","shell.execute_reply":"2024-07-28T21:12:03.833147Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"forecasted_rh_values.to_excel('forecasted_rh_values_to 2050.xlsx')","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:06.566278Z","iopub.execute_input":"2024-07-28T21:12:06.566711Z","iopub.status.idle":"2024-07-28T21:12:08.242391Z","shell.execute_reply.started":"2024-07-28T21:12:06.566678Z","shell.execute_reply":"2024-07-28T21:12:08.241189Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"forecasted_rh_values.tail(50)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:10.483499Z","iopub.execute_input":"2024-07-28T21:12:10.484022Z","iopub.status.idle":"2024-07-28T21:12:10.496324Z","shell.execute_reply.started":"2024-07-28T21:12:10.483985Z","shell.execute_reply":"2024-07-28T21:12:10.494917Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"2050-12-13    90.124463\n2050-12-14    90.121146\n2050-12-15    90.171027\n2050-12-16    90.133149\n2050-12-17    90.046076\n2050-12-18    89.934656\n2050-12-19    89.908711\n2050-12-20    90.094886\n2050-12-21    90.083209\n2050-12-22    89.996705\n2050-12-23    90.116468\n2050-12-24    90.034291\n2050-12-25    90.130537\n2050-12-26    90.127220\n2050-12-27    90.177101\n2050-12-28    90.139223\n2050-12-29    90.052150\n2050-12-30    89.940730\n2050-12-31    89.914785\n2051-01-01    90.100960\n2051-01-02    90.089283\n2051-01-03    90.002780\n2051-01-04    90.122543\n2051-01-05    90.040365\n2051-01-06    90.136611\n2051-01-07    90.133295\n2051-01-08    90.183176\n2051-01-09    90.145297\n2051-01-10    90.058224\n2051-01-11    89.946804\n2051-01-12    89.920859\n2051-01-13    90.107034\n2051-01-14    90.095358\n2051-01-15    90.008854\n2051-01-16    90.128617\n2051-01-17    90.046440\n2051-01-18    90.142685\n2051-01-19    90.139369\n2051-01-20    90.189250\n2051-01-21    90.151372\n2051-01-22    90.064298\n2051-01-23    89.952879\n2051-01-24    89.926934\n2051-01-25    90.113108\n2051-01-26    90.101432\n2051-01-27    90.014928\n2051-01-28    90.134691\n2051-01-29    90.052514\n2051-01-30    90.148760\n2051-01-31    90.145443\nFreq: D, Name: predicted_mean, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"# Combine the forecasted values with the original data\nforecasted_rh_series = pd.Series(forecasted_rh_values, index=forecast_rh_index)\ncombined_rh_data = pd.concat([data_rh, forecasted_rh_series.rename('Forecasted_Relative_Humidity')])","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:15.820146Z","iopub.execute_input":"2024-07-28T21:12:15.821499Z","iopub.status.idle":"2024-07-28T21:12:15.834586Z","shell.execute_reply.started":"2024-07-28T21:12:15.821452Z","shell.execute_reply":"2024-07-28T21:12:15.833153Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"# Save the forecasted data and the combined data to an Excel file\ncombined_rh_data.to_excel('combined_forecasted_rh_data.xlsx')","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:25.264749Z","iopub.execute_input":"2024-07-28T21:12:25.265777Z","iopub.status.idle":"2024-07-28T21:12:27.948895Z","shell.execute_reply.started":"2024-07-28T21:12:25.265732Z","shell.execute_reply":"2024-07-28T21:12:27.947701Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"forecasted_rh_series.head(30)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:29.295150Z","iopub.execute_input":"2024-07-28T21:12:29.295589Z","iopub.status.idle":"2024-07-28T21:12:29.306024Z","shell.execute_reply.started":"2024-07-28T21:12:29.295556Z","shell.execute_reply":"2024-07-28T21:12:29.304580Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"2024-01-01    85.009748\n2024-02-01    85.077341\n2024-03-01    85.040119\n2024-04-01    85.107712\n2024-05-01    85.190253\n2024-06-01    85.026664\n2024-07-01    85.138448\n2024-08-01    85.031090\n2024-09-01    85.261748\n2024-10-01    85.247636\n2024-11-01    85.342000\n2024-12-01    85.266331\n2025-01-01    85.334493\n2025-02-01    85.329961\n2025-03-01    85.402743\n2025-04-01    85.240569\n2025-05-01    85.395236\n2025-06-01    85.390704\n2025-07-01    85.338534\n2025-08-01    85.338898\n2025-09-01    85.231540\n2025-10-01    85.465514\n2025-11-01    85.448086\n2025-12-01    85.492569\n2026-01-01    85.466781\n2026-02-01    85.534943\n2026-03-01    85.508829\n2026-04-01    85.603193\n2026-05-01    85.527523\n2026-06-01    85.595686\nFreq: MS, Name: predicted_mean, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"forecasted_rh_series.tail(30)","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:35.291701Z","iopub.execute_input":"2024-07-28T21:12:35.292121Z","iopub.status.idle":"2024-07-28T21:12:35.301696Z","shell.execute_reply.started":"2024-07-28T21:12:35.292092Z","shell.execute_reply":"2024-07-28T21:12:35.300512Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"2048-07-01    89.479087\n2048-08-01    89.687117\n2048-09-01    89.669688\n2048-10-01    89.714171\n2048-11-01    89.688383\n2048-12-01    89.794424\n2049-01-01    89.632251\n2049-02-01    89.699844\n2049-03-01    89.749125\n2049-04-01    89.817288\n2049-05-01    89.692993\n2049-06-01    89.760586\n2049-07-01    89.843127\n2049-08-01    89.679537\n2049-09-01    89.887567\n2049-10-01    89.683964\n2049-11-01    89.914622\n2049-12-01    89.900510\n2050-01-01    89.994874\n2050-02-01    89.832701\n2050-03-01    89.975364\n2050-04-01    89.949576\n2050-05-01    90.055616\n2050-06-01    89.893443\n2050-07-01    90.048110\n2050-08-01    90.043577\n2050-09-01    89.879988\n2050-10-01    89.991772\n2050-11-01    89.884414\n2050-12-01    90.118388\nFreq: MS, Name: predicted_mean, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"combined_rh_data.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:47.704254Z","iopub.execute_input":"2024-07-28T21:12:47.704857Z","iopub.status.idle":"2024-07-28T21:12:47.722356Z","shell.execute_reply.started":"2024-07-28T21:12:47.704814Z","shell.execute_reply":"2024-07-28T21:12:47.720907Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"            Relative_Humidity  Forecasted_Relative_Humidity\n1982-01-01              79.75                           NaN\n1982-01-02              77.31                           NaN\n1982-01-03              83.69                           NaN\n1982-01-04              77.31                           NaN\n1982-01-05              64.94                           NaN","text/html":"<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>Relative_Humidity</th>\n      <th>Forecasted_Relative_Humidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1982-01-01</th>\n      <td>79.75</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1982-01-02</th>\n      <td>77.31</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1982-01-03</th>\n      <td>83.69</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1982-01-04</th>\n      <td>77.31</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1982-01-05</th>\n      <td>64.94</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"combined_rh_data.tail()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T21:12:56.925097Z","iopub.execute_input":"2024-07-28T21:12:56.926560Z","iopub.status.idle":"2024-07-28T21:12:56.939860Z","shell.execute_reply.started":"2024-07-28T21:12:56.926512Z","shell.execute_reply":"2024-07-28T21:12:56.938230Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"                     Relative_Humidity  Forecasted_Relative_Humidity\n2050-08-01 00:00:00                NaN                     90.043577\n2050-09-01 00:00:00                NaN                     89.879988\n2050-10-01 00:00:00                NaN                     89.991772\n2050-11-01 00:00:00                NaN                     89.884414\n2050-12-01 00:00:00                NaN                     90.118388","text/html":"<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>Relative_Humidity</th>\n      <th>Forecasted_Relative_Humidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2050-08-01 00:00:00</th>\n      <td>NaN</td>\n      <td>90.043577</td>\n    </tr>\n    <tr>\n      <th>2050-09-01 00:00:00</th>\n      <td>NaN</td>\n      <td>89.879988</td>\n    </tr>\n    <tr>\n      <th>2050-10-01 00:00:00</th>\n      <td>NaN</td>\n      <td>89.991772</td>\n    </tr>\n    <tr>\n      <th>2050-11-01 00:00:00</th>\n      <td>NaN</td>\n      <td>89.884414</td>\n    </tr>\n    <tr>\n      <th>2050-12-01 00:00:00</th>\n      <td>NaN</td>\n      <td>90.118388</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Save the model\nimport pickle\n\nwith open('/kaggle/working/sarima_rh_model.pkl', 'wb') as f:\n    pickle.dump(model, f)","metadata":{"execution":{"iopub.status.busy":"2024-07-19T19:24:49.772288Z","iopub.execute_input":"2024-07-19T19:24:49.773825Z","iopub.status.idle":"2024-07-19T19:24:52.313784Z","shell.execute_reply.started":"2024-07-19T19:24:49.773775Z","shell.execute_reply":"2024-07-19T19:24:52.312339Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# Plot the results\nplt.figure(figsize=(12, 6))\nplt.plot(data_rh.index, data_rh['Relative_Humidity'], label='Observed', color='blue')\n#plt.plot(forecasted_rh_series.index, forecasted_rh_series, label='Forecasted', color='green')\nplt.xlabel('Date')\nplt.ylabel('Relative Humidity')\nplt.title('Relative Humidity Forecast from 2023 to 2050')\n#plt.legend()\n#plt.savefig('/kaggle/working/Figure_OSUN_RH_SARIMAX1.png', dpi=300, transparent=True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-28T20:35:51.766663Z","iopub.execute_input":"2024-07-28T20:35:51.767988Z","iopub.status.idle":"2024-07-28T20:36:48.459946Z","shell.execute_reply.started":"2024-07-28T20:35:51.767942Z","shell.execute_reply":"2024-07-28T20:36:48.457955Z"},"trusted":true},"execution_count":49,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[49], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRelative Humidity Forecast from 2023 to 2050\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m#plt.legend()\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m#plt.savefig('/kaggle/working/Figure_OSUN_RH_SARIMAX1.png', dpi=300, transparent=True)\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/pyplot.py:446\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    404\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:179\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    177\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:223\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/axis.py:1392\u001b[0m, in \u001b[0;36mAxis.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1389\u001b[0m tlb1, tlb2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ticklabel_bboxes(ticks_to_draw, renderer)\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tick \u001b[38;5;129;01min\u001b[39;00m ticks_to_draw:\n\u001b[0;32m-> 1392\u001b[0m     \u001b[43mtick\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1394\u001b[0m \u001b[38;5;66;03m# Shift label away from axes to avoid overlapping ticklabels.\u001b[39;00m\n\u001b[1;32m   1395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_label_position(renderer)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/axis.py:301\u001b[0m, in \u001b[0;36mTick.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    298\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, gid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m    299\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgridline, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtick1line, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtick2line,\n\u001b[1;32m    300\u001b[0m                \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel1, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel2]:\n\u001b[0;32m--> 301\u001b[0m     \u001b[43martist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    302\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/text.py:752\u001b[0m, in \u001b[0;36mText.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    749\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cm_set(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_wrapped_text()):\n\u001b[0;32m--> 752\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    753\u001b[0m     trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    755\u001b[0m     \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[1;32m    756\u001b[0m     \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/text.py:378\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    375\u001b[0m ys \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    377\u001b[0m \u001b[38;5;66;03m# Full vertical extent of font, including ascenders and descenders:\u001b[39;00m\n\u001b[0;32m--> 378\u001b[0m _, lp_h, lp_d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m    \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTeX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_usetex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m min_dy \u001b[38;5;241m=\u001b[39m (lp_h \u001b[38;5;241m-\u001b[39m lp_d) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_linespacing\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, line \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lines):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/text.py:98\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _get_text_metrics_with_cache_impl(\n\u001b[0;32m---> 98\u001b[0m     weakref\u001b[38;5;241m.\u001b[39mref(renderer), text, \u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, ismath, dpi)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/matplotlib/font_manager.py:896\u001b[0m, in \u001b[0;36mFontProperties.copy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcopy\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    895\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a copy of self.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 896\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcopy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/copy.py:102\u001b[0m, in \u001b[0;36mcopy\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rv, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    255\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    256\u001b[0m         \u001b[38;5;66;03m# aha, this is the first one :-)\u001b[39;00m\n\u001b[1;32m    257\u001b[0m         memo[\u001b[38;5;28mid\u001b[39m(memo)]\u001b[38;5;241m=\u001b[39m[x]\n\u001b[0;32m--> 259\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_reconstruct\u001b[39m(x, memo, func, args,\n\u001b[1;32m    260\u001b[0m                  state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, listiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dictiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    261\u001b[0m                  \u001b[38;5;241m*\u001b[39m, deepcopy\u001b[38;5;241m=\u001b[39mdeepcopy):\n\u001b[1;32m    262\u001b[0m     deep \u001b[38;5;241m=\u001b[39m memo \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    263\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m deep \u001b[38;5;129;01mand\u001b[39;00m args:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"markdown","source":"## Model Evaluation","metadata":{}},{"cell_type":"code","source":"# Calculate MSE, RMSE, MAE, and MAPE\ndef mean_absolute_percentage_error(y_true, y_pred):\n    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100","metadata":{"execution":{"iopub.status.busy":"2024-06-09T13:02:57.473551Z","iopub.execute_input":"2024-06-09T13:02:57.473993Z","iopub.status.idle":"2024-06-09T13:02:57.480251Z","shell.execute_reply.started":"2024-06-09T13:02:57.473960Z","shell.execute_reply":"2024-06-09T13:02:57.479055Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# Since the test set is empty, we'll use the training set for evaluation in this example\ny_true = train['Relative_Humidity']\ny_pred = sarima_model.fittedvalues","metadata":{"execution":{"iopub.status.busy":"2024-06-09T13:03:03.421378Z","iopub.execute_input":"2024-06-09T13:03:03.421760Z","iopub.status.idle":"2024-06-09T13:03:03.427881Z","shell.execute_reply.started":"2024-06-09T13:03:03.421734Z","shell.execute_reply":"2024-06-09T13:03:03.426552Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"mse = mean_squared_error(y_true, y_pred)\nrmse = np.sqrt(mse)\nmae = mean_absolute_error(y_true, y_pred)\nmape = mean_absolute_percentage_error(y_true, y_pred)\n\nprint(f'MSE: {mse}')\nprint(f'RMSE: {rmse}')\nprint(f'MAE: {mae}')\nprint(f'MAPE: {mape}')\n\n# Since we need to ensure all arrays contain the same number of samples, let's check\nprint(f'y_true size: {len(y_true)}, y_pred size: {len(y_pred)}')","metadata":{"execution":{"iopub.status.busy":"2024-06-09T13:03:11.404489Z","iopub.execute_input":"2024-06-09T13:03:11.405634Z","iopub.status.idle":"2024-06-09T13:03:11.415989Z","shell.execute_reply.started":"2024-06-09T13:03:11.405593Z","shell.execute_reply":"2024-06-09T13:03:11.414644Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"MSE: 30.11457554087808\nRMSE: 5.487674875653448\nMAE: 3.0074469920796707\nMAPE: 4.251824578992422\ny_true size: 504, y_pred size: 504\n","output_type":"stream"}]}]}