Weather and Satellite Plots

satelliteBurnPlot.py

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+import pandas as pd 
+import seaborn as sns
+import matplotlib.pyplot as plt 
+import plotly.express as px
+
+def load_and_prepare_burn_data(file_paths):
+    all_data = []
+
+    for file_path in file_paths:
+        group_number = int(file_path.split('_')[-1].split('.')[0])
+
+        year = 2017 + (group_number - 1) // 4
+        quarter = 'Q' + str(((group_number - 1) % 4) + 1)
+
+        df = pd.read_csv(file_path, header=None, names=['hectares_burned'])
+
+        df['year'] = year
+        df['quarter'] = quarter
+
+        all_data.append(df)
+
+    combined_data = pd.concat(all_data, ignore_index=True)
+
+    return combined_data
+
+burn_file_paths = [f'data/training/quarterly-6-by-6/satellite-burn/group_{i}.csv' for i in range(1, 29)]  # Files from group_1 to group_10
+burn_data = load_and_prepare_burn_data(burn_file_paths)
+
+def create_burn_box_plots(data):
+
+    sns.set_style("whitegrid")
+
+    plt.figure(figsize=(12, 6))
+
+    sns.boxplot(x='quarter', y='hectares_burned', hue='year', data=data, palette='pastel', showfliers=False)
+
+    plt.title('Hectares Burned vs Quarters', fontsize=16)
+    plt.xlabel('Quarter', fontsize=14)
+    plt.ylabel('Hectares Burned', fontsize=14)
+
+
+    plt.legend(title='Year', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)
+
+
+    plt.ylim(0, 5000)
+    plt.tight_layout()
+    plt.savefig('data/satellite_burn_box_plots_capped.png')
+
+    # Show the plot
+    plt.show()
+
+
+def create_burn_box_plots_interactive(data):
+    fig = px.box(data, x='quarter', y='hectares_burned', color='year',
+                 labels={'hectares_burned': 'Hectares Burned', 'quarter': 'Quarter'},
+                 title='Hectares Burned vs Quarters')
+    fig.update_traces(quartilemethod="inclusive")  # Or "exclusive", depending on how you want to calculate quartiles
+    fig.update_layout(yaxis=dict(range=[0, 25000]))
+    fig.show()
+
+
+#create_burn_box_plots_interactive(burn_data)
+create_burn_box_plots(burn_data)

weatherPlot.py

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+import pandas as pd 
+import seaborn as sns
+import matplotlib.pyplot as plt 
+
+def load_and_prepare_data(file_paths):
+    all_data = []
+
+    for file_path in file_paths:
+        group_number = int(file_path.split('_')[-1].split('.')[0])
+        year = 2017 + (group_number - 1) // 4
+        quarter = 'Q' + str(((group_number - 1) % 4) + 1)
+
+        df = pd.read_csv(file_path)
+        df['year'] = year
+        df['quarter'] = quarter
+
+        all_data.append(df)
+
+    combined_data = pd.concat(all_data, ignore_index=True)
+
+    return combined_data
+
+file_paths = [f'data/training/quarterly-6-by-6/weather/group_{i}.csv' for i in range(1, 25)]
+weather_data = load_and_prepare_data(file_paths)
+
+def create_individual_plots(data, parameters):
+    sns.set_style("whitegrid")
+
+    for parameter in parameters:
+        plt.figure(figsize=(12, 6))
+        sns.boxplot(x='quarter', y=parameter, hue='year', data=data, palette='pastel')
+        plt.title(f'{parameter.replace("_", " ").capitalize()} vs Quarters', fontsize=16)
+        plt.xlabel('Quarter', fontsize=14)
+        plt.ylabel(parameter.replace("_", " ").capitalize(), fontsize=14)
+        plt.legend(title='Year', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)
+        plt.tight_layout()
+
+        # Save each plot to a separate file
+        plt.savefig(f'data/{parameter}_box_plot.png')
+        plt.close()  # Close the plot to free up memory
+
+parameters = ['precipitation', 'temperature', 'relative_humidity', 'wind_direction', 'wind_speed']
+
+create_individual_plots(weather_data, parameters)