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| # -*- coding: utf-8 -*- | |
| """app.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1I5SzMTkFWVzSyzaZNkQcYqUi7F9Nzxpx | |
| """ | |
| import gradio as gr | |
| import xarray as xr | |
| import pandas as pd | |
| from statsmodels.tsa.statespace.sarimax import SARIMAX | |
| from sklearn.metrics import mean_squared_error | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from datetime import datetime | |
| import fsspec | |
| # Filepath for NASA POWER data | |
| FILEPATH = 'https://power-analysis-ready-datastore.s3.amazonaws.com/power_901_monthly_radiation_utc.zarr' | |
| # Load dataset | |
| def load_dataset(filepath): | |
| filepath_mapped = fsspec.get_mapper(filepath) | |
| return xr.open_zarr(store=filepath_mapped, consolidated=True) | |
| dataset = load_dataset(FILEPATH) | |
| # Function for SARIMA forecasting | |
| def predict_forecast(lat, lon, start_date, end_date, forecast_steps): | |
| try: | |
| # Find nearest latitude and longitude | |
| nearest_lat = dataset.lat.sel(lat=lat, method='nearest').item() | |
| nearest_lon = dataset.lon.sel(lon=lon, method='nearest').item() | |
| # Slice data for nearest lat/lon and the time range | |
| ds_time_series = dataset.ALLSKY_SFC_LW_DWN.sel( | |
| lat=nearest_lat, lon=nearest_lon, time=slice(start_date, end_date) | |
| ).load() | |
| data_series = ds_time_series.values | |
| dates = pd.to_datetime(ds_time_series.time.values) | |
| # Split into training and testing | |
| train_size = int(len(data_series) * 0.8) | |
| train, test = data_series[:train_size], data_series[train_size:] | |
| # Fit SARIMA model | |
| model = SARIMAX(train, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12)) | |
| result = model.fit(disp=False) | |
| forecast = result.forecast(steps=forecast_steps) | |
| # Calculate RMSE | |
| rmse = np.sqrt(mean_squared_error(test[:len(forecast)], forecast)) | |
| # Create a plot | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(dates[train_size:train_size + len(test)], test, label='Actual') | |
| plt.plot(dates[train_size:train_size + len(forecast)], forecast, label='Forecast', linestyle='--') | |
| plt.legend() | |
| plt.title(f"Forecast vs Actual (RMSE: {rmse:.2f})") | |
| plt.xlabel("Date") | |
| plt.ylabel("Solar Radiation") | |
| plt.savefig("forecast_plot.png") | |
| return { | |
| "RMSE": rmse, | |
| "Forecast": forecast.tolist(), | |
| "Plot": "forecast_plot.png" | |
| } | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Gradio app | |
| def gradio_interface(lat, lon, start_date, end_date, forecast_steps): | |
| result = predict_forecast(lat, lon, start_date, end_date, forecast_steps) | |
| if isinstance(result, dict): # If successful | |
| return result["RMSE"], result["Forecast"], result["Plot"] | |
| else: # If error | |
| return result, None, None | |
| interface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs=[ | |
| gr.Number(label="Latitude"), | |
| gr.Number(label="Longitude"), | |
| gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="2019-01-01"), | |
| gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="2024-12-31"), | |
| gr.Number(label="Forecast Steps (e.g., 12 for 1 year)") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="RMSE"), | |
| gr.Textbox(label="Forecast Values"), | |
| gr.Image(label="Forecast Plot") | |
| ], | |
| title="Solar Radiation Forecast with SARIMA", | |
| description="Enter location and time range to forecast solar radiation using SARIMA." | |
| ) | |
| # Launch the app | |
| interface.launch() |