SolarSim/visualization.py at main · WeRaDev/SolarSim · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.dates import DateFormatter
import os
from typing import List, Union
from config import load_config

# Add seaborn for improved plot aesthetics
sns.set_style("whitegrid")

def ensure_charts_directory():
    if not os.path.exists('charts'):
        os.makedirs('charts')

def load_data():
    data_path = os.path.join(os.path.dirname(__file__), 'data', 'simulation_data.csv')
    df = pd.read_csv(data_path, parse_dates=['datetime'])
    df.set_index('datetime', inplace=True)
    return df

# Updated plot_chart function with more flexibility and improved aesthetics
def plot_chart(df: pd.DataFrame, x: str, y: Union[str, List[str]], title: str, xlabel: str, ylabel: str, scale: str = 'year', kind: str = 'line'):
    plt.figure(figsize=(12, 6))

    if kind == 'line':
        if isinstance(y, list):
            for col in y:
                sns.lineplot(data=df, x=x, y=col, label=col)
        else:
            sns.lineplot(data=df, x=x, y=y, label=y)
    elif kind == 'scatter':
        if isinstance(y, list):
            for col in y:
                sns.scatterplot(data=df, x=x, y=col, label=col, alpha=0.6)
        else:
            sns.scatterplot(data=df, x=x, y=y, label=y, alpha=0.6)
    elif kind == 'area':
        if isinstance(y, list):
            df[y].plot.area(stacked=True, alpha=0.7)
        else:
            df[y].plot.area(alpha=0.7, label=y)

    plt.title(f"{title} ({scale.capitalize()} Scale)", fontsize=16)
    plt.xlabel(xlabel, fontsize=12)
    plt.ylabel(ylabel, fontsize=12)

    if scale == 'year':
        plt.gca().xaxis.set_major_formatter(DateFormatter('%Y-%m'))
    elif scale == 'week':
        plt.gca().xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
    elif scale == 'day':
        plt.gca().xaxis.set_major_formatter(DateFormatter('%H:%M'))

    plt.xticks(rotation=45)

    # Only add legend if there are labeled artists
    if plt.gca().get_legend_handles_labels()[0]:
        plt.legend(fontsize=10)

    plt.tight_layout()

    ensure_charts_directory()
    filename = f"charts/{title.lower().replace(' ', '_')}_{scale}.png"
    plt.savefig(filename, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Saved chart: {filename}")

# Remove unused functions
# Removed plot_energy_data and plot_battery_profile as they're not used in generate_charts

def generate_charts():
    config = load_config()
    year = config.year

    ensure_charts_directory()
    df = load_data()

    # Calculate available energy for the entire dataset
    df['available_energy'] = df['production'] + df['battery_charge']

    # Define time ranges
    summer_week = df.loc[f'{year}-08-15':f'{year}-08-22'].copy()
    winter_week = df.loc[f'{year}-03-15':f'{year}-03-22'].copy()
    summer_day = df.loc[f'{year}-08-23'].copy()
    winter_day = df.loc[f'{year}-03-23'].copy()

    # Calculate available energy for each subset
    for subset in [summer_week, winter_week, summer_day, winter_day]:
        subset['available_energy'] = subset['production'] + subset['battery_charge']

    # 1. Energy production vs weather conditions
    weather_conditions = ['sun_intensity', 'temperature', 'humidity', 'cloud_cover', 'wind_speed']
    for condition in weather_conditions:
        plot_chart(df, condition, 'production', f'Energy Production vs {condition.capitalize()}', condition, 'Energy Production (kWh)', 'year', kind='scatter')
        plot_chart(summer_week, condition, 'production', f'Energy Production vs {condition.capitalize()}', condition, 'Energy Production (kWh)', 'week', kind='scatter')
        plot_chart(winter_week, condition, 'production', f'Energy Production vs {condition.capitalize()}', condition, 'Energy Production (kWh)', 'week', kind='scatter')
        plot_chart(summer_day, condition, 'production', f'Energy Production vs {condition.capitalize()}', condition, 'Energy Production (kWh)', 'day', kind='scatter')
        plot_chart(winter_day, condition, 'production', f'Energy Production vs {condition.capitalize()}', condition, 'Energy Production (kWh)', 'day', kind='scatter')

    # 2. Energy production versus consumption
    plot_chart(df, 'datetime', ['production', 'total_consumption'], 'Energy Production vs Consumption', 'Date', 'Energy (kWh)', 'year')
    plot_chart(summer_week, 'datetime', ['production', 'total_consumption'], 'Energy Production vs Consumption', 'Date', 'Energy (kWh)', 'week')
    plot_chart(winter_week, 'datetime', ['production', 'total_consumption'], 'Energy Production vs Consumption', 'Date', 'Energy (kWh)', 'week')
    plot_chart(summer_day, 'datetime', ['production', 'total_consumption'], 'Energy Production vs Consumption', 'Date', 'Energy (kWh)', 'day')
    plot_chart(winter_day, 'datetime', ['production', 'total_consumption'], 'Energy Production vs Consumption', 'Date', 'Energy (kWh)', 'day')

    # 3. Energy profile vs allocation
    energy_components = ['irrigation', 'servers', 'gpu']
    plot_chart(df, 'datetime', energy_components, 'Energy Profile vs Allocation', 'Date', 'Energy (kWh)', 'year', kind='area')
    plot_chart(summer_week, 'datetime', energy_components, 'Energy Profile vs Allocation', 'Date', 'Energy (kWh)', 'week', kind='area')
    plot_chart(winter_week, 'datetime', energy_components, 'Energy Profile vs Allocation', 'Date', 'Energy (kWh)', 'week', kind='area')
    plot_chart(summer_day, 'datetime', energy_components, 'Energy Profile vs Allocation', 'Date', 'Energy (kWh)', 'day', kind='area')
    plot_chart(winter_day, 'datetime', energy_components, 'Energy Profile vs Allocation', 'Date', 'Energy (kWh)', 'day', kind='area')

    # 4. Available energy vs energy profile
    plot_chart(df, 'datetime', ['available_energy', 'total_consumption', 'battery_charge'], 'Available Energy vs Energy Profile', 'Date', 'Energy (kWh)', 'year')
    plot_chart(summer_week, 'datetime', ['available_energy', 'total_consumption', 'battery_charge'], 'Available Energy vs Energy Profile', 'Date', 'Energy (kWh)', 'week')
    plot_chart(winter_week, 'datetime', ['available_energy', 'total_consumption', 'battery_charge'], 'Available Energy vs Energy Profile', 'Date', 'Energy (kWh)', 'week')
    plot_chart(summer_day, 'datetime', ['available_energy', 'total_consumption', 'battery_charge'], 'Available Energy vs Energy Profile', 'Date', 'Energy (kWh)', 'day')
    plot_chart(winter_day, 'datetime', ['available_energy', 'total_consumption', 'battery_charge'], 'Available Energy vs Energy Profile', 'Date', 'Energy (kWh)', 'day')

if __name__ == "__main__":
    generate_charts()