Merge branch 'main' of https://huggingface.co/spaces/Redhotchilipoppy/solprognos

app.py

@@ -1,165 +1,165 @@
-# Import gradio
-import gradio as gr
-
-# Data import from open-meteo.com
-import openmeteo_requests               
-import requests_cache
-from retry_requests import retry
-    
-# Data management and visualization
-import pandas as pd
-import numpy as np
-import datetime
-import pickle
-import matplotlib
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-
-# Machine Learning
-import sklearn
-
-# Print version info
-print("Version info:")
-print('pandas: %s' % pd.__version__)
-print('numpy: %s' % np.__version__)
-print('sklearn: %s' % sklearn.__version__)
-print(" ")
-
-
-def makeprediction():
-#%% 1. User Inputs
-    # These inputs may be changed by the user
-    model_file_name = "Solar RF Model_2024-01-03T08-53-53_.pkl"    # This is the name of the file generated by the script "Build_solar_RF_model.py" (must be located in same folder)
-    horizon = 2                                                    # Forecast horizon in days, limit is 16 days.
-                                                                   # Note that the further we look into the future, the less accurate the weather forecast is likely to be
-    PV_capacity = 15                                               # The maximum capacity of your PV installation in kW, only used for plotting.
-    
-    #%% 2. Read Model
-    # Read in the model
-    f_model = open(model_file_name, 'rb')               # Opens the file
-    model_dict = pickle.load(f_model)                   # Reads the dictionary from the file
-    f_model.close()                                     # closes the file again
-    
-    #%% 3. Fetch weather forecast
-    
-    # Setup the Open-Meteo API client with cache and retry on error
-    cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
-    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
-    openmeteo = openmeteo_requests.Client(session = retry_session)
-    
-    # Make sure all required weather variables are listed here
-    url = "https://api.open-meteo.com/v1/forecast"
-    
-    # Calcualte start and end date
-    start_date = datetime.datetime.now()
-    end_date = start_date + datetime.timedelta(days=horizon)
-    sdate_str = str(start_date)[0:10]                       # First date in file as string (yyyy-mm-dd)
-    edate_str = str(end_date)[0:10]                         # Last date in file as string (yyyy-mm-dd)
-    
-    # Use the same parameters as was used for training the model.
-    params = {
-    	"latitude": model_dict['API Request Params']['latitude'],
-    	"longitude": model_dict['API Request Params']['longitude'],
-    	"hourly": model_dict['API Request Params']['hourly'],
-        "start_date": sdate_str,
-        "end_date": edate_str,
-    }
-    responses = openmeteo.weather_api(url, params=params)
-    
-    # Process & print request info.
-    response = responses[0]
-    print("Request info:")
-    print(f"Coordinates {response.Latitude()}°E {response.Longitude()}°N")
-    print(f"Elevation {response.Elevation()} m asl")
-    print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
-    print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")
-    #print(f"From: " + sdate_str + " To: " + edate_str)
-    print(" ")
-    
-    # Process hourly data. The order of variables needs to be the same as requested.
-    hourly = response.Hourly()                                  # API Response
-    hourly_data = {"date": pd.date_range(                       # Dictionary that we add the API respnse to
-    	start = pd.to_datetime(hourly.Time(), unit = "s"),
-    	end = pd.to_datetime(hourly.TimeEnd(), unit = "s"),
-    	freq = pd.Timedelta(seconds = hourly.Interval()),
-    	inclusive = "left"
-    )}
-    
-    # We iterate through the variables and add the API response data to our dictionary
-    print("Adding variables to dataframe...")
-    index_variable = 0
-    for variable in params['hourly']:
-        hourly_data[variable] = hourly.Variables(index_variable).ValuesAsNumpy()    # Add the variable to dataframe
-        print("Added " + variable)
-        index_variable += 1                                                         # Increment counter
-        
-    print(" ")
-    # Create dataframe
-    weather_data = pd.DataFrame(data = hourly_data)    
-    weather_data = weather_data.set_index('date')    # Set index to be dates
-    
-    #%% 4. Data manipulation & encoding
-    # Create a new dataframe to hold all data, also encode new features.
-    
-    # Create a main dataframe that holds all data
-    main_df = weather_data.copy()
-    
-    for index, row in main_df.iterrows():
-        main_df.loc[index,'month'] = index.month
-        main_df.loc[index,'day'] = index.day
-        main_df.loc[index,'hour'] = index.hour
-        main_df.loc[index,'sine month'] = np.sin((index.month - 1)*np.pi/11)
-        main_df.loc[index,'cos month'] = np.cos((index.month - 1)*np.pi/11)
-        main_df.loc[index,'sine hour'] = np.sin((index.month - 1)*np.pi/23)
-        main_df.loc[index,'cos hour'] = np.cos((index.month - 1)*np.pi/23)
-    
-    # Index to keep track of which hours the sun is up for.        
-    Sun_index = main_df[main_df['is_day'] == 1].index        
-    
-    # Create a new dataframe with only relevant data, this will be used for statistical 
-    sun_is_up_data = main_df.copy()                                 # Create copy
-    sun_is_up_data = sun_is_up_data.loc[Sun_index]                  # Only include rows when the sun is up
-    sun_is_up_data = sun_is_up_data[model_dict['Input features']]   # Only include columns needed for model
-    
-    
-    #%% 5. Make Predictions
-    print("Making predictions...")
-    # Create dataframe to hold predictions and initialize with 0.
-    predictions = pd.DataFrame(0, index = weather_data.index, columns = ['Main Forecast', 'Lower Bound', 'Upper Bound'])
-    predictions['Date'] = predictions.index.tolist()             # Make dates also to a column for easier plotting
-    
-    # Make predictions
-    predictions.loc[Sun_index,'Main Forecast'] = model_dict['Random Forest Model'].predict(sun_is_up_data)
-    predictions.loc[Sun_index,['Lower Bound', 'Upper Bound']] = model_dict['Random Forest Quantile Model'].predict(sun_is_up_data, quantiles=[0.1, 0.9], interpolation='linear')
-    print(" ")
-    #%% 6. Make Plot
-    
-    # Plotting
-    plt.style.use('_mpl-gallery')                   # Set plot style
-    matplotlib.rc('font', **{'size'   : 20})        # Change font size
-    fig, ax = plt.subplots()                        # Create plot
-    fig.set_size_inches(18.5, 10.5, forward=True)   # Set figure size
-    fig.set_dpi(100)                                # Set resolution
-    ax.fill_between(predictions['Date'], predictions['Lower Bound'], predictions['Upper Bound'], alpha=.5, linewidth=0) # Plot prediction interval
-    ax.plot(predictions['Date'], predictions['Main Forecast'], linewidth=4, color='black')                              # Plot main forecast
-    
-    # Calculate x-ticks
-    step_length = int(predictions.shape[0]/32)
-    ax.set(ylim = [0,PV_capacity*1.05], xticks = predictions['Date'].iloc[::step_length])
-    ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
-    plt.ylabel('Solar power [kW]')
-    plt.xticks(rotation=90)
-    plt.legend(['Prediction Interval (10 - 90%)', 'Main Forecast'])
-    plt.title('Solar Power Forecast from ' + sdate_str + ' to ' + edate_str)
-    # plt.show()
-    
-    return fig
-
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-
-
-iface = gr.Interface(fn=makeprediction, inputs=None, outputs=gr.Plot())
+# Import gradio
+import gradio as gr
+
+# Data import from open-meteo.com
+import openmeteo_requests               
+import requests_cache
+from retry_requests import retry
+    
+# Data management and visualization
+import pandas as pd
+import numpy as np
+import datetime
+import pickle
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+# Machine Learning
+import sklearn
+
+# Print version info
+print("Version info:")
+print('pandas: %s' % pd.__version__)
+print('numpy: %s' % np.__version__)
+print('sklearn: %s' % sklearn.__version__)
+print(" ")
+
+
+def makeprediction():
+#%% 1. User Inputs
+    # These inputs may be changed by the user
+    model_file_name = "Solar RF Model_2024-01-03T08-53-53_.pkl"    # This is the name of the file generated by the script "Build_solar_RF_model.py" (must be located in same folder)
+    horizon = 2                                                    # Forecast horizon in days, limit is 16 days.
+                                                                   # Note that the further we look into the future, the less accurate the weather forecast is likely to be
+    PV_capacity = 15                                               # The maximum capacity of your PV installation in kW, only used for plotting.
+    
+    #%% 2. Read Model
+    # Read in the model
+    f_model = open(model_file_name, 'rb')               # Opens the file
+    model_dict = pickle.load(f_model)                   # Reads the dictionary from the file
+    f_model.close()                                     # closes the file again
+    
+    #%% 3. Fetch weather forecast
+    
+    # Setup the Open-Meteo API client with cache and retry on error
+    cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
+    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
+    openmeteo = openmeteo_requests.Client(session = retry_session)
+    
+    # Make sure all required weather variables are listed here
+    url = "https://api.open-meteo.com/v1/forecast"
+    
+    # Calcualte start and end date
+    start_date = datetime.datetime.now()
+    end_date = start_date + datetime.timedelta(days=horizon)
+    sdate_str = str(start_date)[0:10]                       # First date in file as string (yyyy-mm-dd)
+    edate_str = str(end_date)[0:10]                         # Last date in file as string (yyyy-mm-dd)
+    
+    # Use the same parameters as was used for training the model.
+    params = {
+    	"latitude": model_dict['API Request Params']['latitude'],
+    	"longitude": model_dict['API Request Params']['longitude'],
+    	"hourly": model_dict['API Request Params']['hourly'],
+        "start_date": sdate_str,
+        "end_date": edate_str,
+    }
+    responses = openmeteo.weather_api(url, params=params)
+    
+    # Process & print request info.
+    response = responses[0]
+    print("Request info:")
+    print(f"Coordinates {response.Latitude()}°E {response.Longitude()}°N")
+    print(f"Elevation {response.Elevation()} m asl")
+    print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
+    print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")
+    #print(f"From: " + sdate_str + " To: " + edate_str)
+    print(" ")
+    
+    # Process hourly data. The order of variables needs to be the same as requested.
+    hourly = response.Hourly()                                  # API Response
+    hourly_data = {"date": pd.date_range(                       # Dictionary that we add the API respnse to
+    	start = pd.to_datetime(hourly.Time(), unit = "s"),
+    	end = pd.to_datetime(hourly.TimeEnd(), unit = "s"),
+    	freq = pd.Timedelta(seconds = hourly.Interval()),
+    	inclusive = "left"
+    )}
+    
+    # We iterate through the variables and add the API response data to our dictionary
+    print("Adding variables to dataframe...")
+    index_variable = 0
+    for variable in params['hourly']:
+        hourly_data[variable] = hourly.Variables(index_variable).ValuesAsNumpy()    # Add the variable to dataframe
+        print("Added " + variable)
+        index_variable += 1                                                         # Increment counter
+        
+    print(" ")
+    # Create dataframe
+    weather_data = pd.DataFrame(data = hourly_data)    
+    weather_data = weather_data.set_index('date')    # Set index to be dates
+    
+    #%% 4. Data manipulation & encoding
+    # Create a new dataframe to hold all data, also encode new features.
+    
+    # Create a main dataframe that holds all data
+    main_df = weather_data.copy()
+    
+    for index, row in main_df.iterrows():
+        main_df.loc[index,'month'] = index.month
+        main_df.loc[index,'day'] = index.day
+        main_df.loc[index,'hour'] = index.hour
+        main_df.loc[index,'sine month'] = np.sin((index.month - 1)*np.pi/11)
+        main_df.loc[index,'cos month'] = np.cos((index.month - 1)*np.pi/11)
+        main_df.loc[index,'sine hour'] = np.sin((index.month - 1)*np.pi/23)
+        main_df.loc[index,'cos hour'] = np.cos((index.month - 1)*np.pi/23)
+    
+    # Index to keep track of which hours the sun is up for.        
+    Sun_index = main_df[main_df['is_day'] == 1].index        
+    
+    # Create a new dataframe with only relevant data, this will be used for statistical 
+    sun_is_up_data = main_df.copy()                                 # Create copy
+    sun_is_up_data = sun_is_up_data.loc[Sun_index]                  # Only include rows when the sun is up
+    sun_is_up_data = sun_is_up_data[model_dict['Input features']]   # Only include columns needed for model
+    
+    
+    #%% 5. Make Predictions
+    print("Making predictions...")
+    # Create dataframe to hold predictions and initialize with 0.
+    predictions = pd.DataFrame(0, index = weather_data.index, columns = ['Main Forecast', 'Lower Bound', 'Upper Bound'])
+    predictions['Date'] = predictions.index.tolist()             # Make dates also to a column for easier plotting
+    
+    # Make predictions
+    predictions.loc[Sun_index,'Main Forecast'] = model_dict['Random Forest Model'].predict(sun_is_up_data)
+    predictions.loc[Sun_index,['Lower Bound', 'Upper Bound']] = model_dict['Random Forest Quantile Model'].predict(sun_is_up_data, quantiles=[0.1, 0.9], interpolation='linear')
+    print(" ")
+    #%% 6. Make Plot
+    
+    # Plotting
+    plt.style.use('_mpl-gallery')                   # Set plot style
+    matplotlib.rc('font', **{'size'   : 20})        # Change font size
+    fig, ax = plt.subplots()                        # Create plot
+    fig.set_size_inches(18.5, 10.5, forward=True)   # Set figure size
+    fig.set_dpi(100)                                # Set resolution
+    ax.fill_between(predictions['Date'], predictions['Lower Bound'], predictions['Upper Bound'], alpha=.5, linewidth=0) # Plot prediction interval
+    ax.plot(predictions['Date'], predictions['Main Forecast'], linewidth=4, color='black')                              # Plot main forecast
+    
+    # Calculate x-ticks
+    step_length = int(predictions.shape[0]/32)
+    ax.set(ylim = [0,PV_capacity*1.05], xticks = predictions['Date'].iloc[::step_length])
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
+    plt.ylabel('Solar power [kW]')
+    plt.xticks(rotation=90)
+    plt.legend(['Prediction Interval (10 - 90%)', 'Main Forecast'])
+    plt.title('Solar Power Forecast from ' + sdate_str + ' to ' + edate_str)
+    # plt.show()
+    
+    return fig
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+
+iface = gr.Interface(fn=makeprediction, inputs=None, outputs=gr.Plot())
 iface.launch()