Moving Average & Sector Frequency Diagram

import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates from scipy.interpolate import make_interp_spline import numpy as np # Load the data df = pd.read_csv('wind_data.csv') # Convert 'datetime' to datetime objects df['datetime'] = pd.to_datetime(df['datetime'], format='\%Y-\%m-\%d') # Task 1: Plot the raw wind direction data as a smooth curve plt.figure(figsize=(12, 6)) plt.plot(df['datetime'], df['winddir'], label='Raw Wind Direction', alpha=0.5) # Creating a smooth curve x_smooth = mdates.date2num(df['datetime']) y_smooth = df['winddir'] # Assuming we want to smooth using a spline with a high number of points spl = make_interp_spline(x_smooth, y_smooth, k=3) # Use a spline of degree 3 xnew = np.linspace(x_smooth.min(), x_smooth.max(), 300) ynew = spl(xnew) plt.plot(mdates.num2date(xnew), ynew, label='Smoothed Curve') plt.xlabel('Date') plt.ylabel('Wind Direction (degrees)') plt.title('Wind Direction Over Time') plt.legend() plt.show()

# Task 2: Calculate and plot the 5-day moving average df['5_day_avg'] = df['winddir'].rolling(window=5, min_periods=1).mean() plt.figure(figsize=(12, 6)) plt.plot(df['datetime'], df['5_day_avg'], label='5-Day Moving Average', color='orange') plt.xlabel('Date') plt.ylabel('Wind Direction (degrees)') plt.title('5-Day Moving Average of Wind Direction') plt.legend() plt.show() # Task 3: Calculate and plot the 30-day moving average df['30_day_avg'] = df['winddir'].rolling(window=30, min_periods=1).mean() plt.figure(figsize=(12, 6)) plt.plot(df['datetime'], df['30_day_avg'], label='30-Day Moving Average', color='green') plt.xlabel('Date') plt.ylabel('Wind Direction (degrees)') plt.title('30-Day Moving Average of Wind Direction') plt.legend() plt.show() # Task 3.1: Calculate and plot the 60-day moving average df['60_day_avg'] = df['winddir'].rolling(window=60, min_periods=1).mean() plt.figure(figsize=(12, 6)) plt.plot(df['datetime'], df['60_day_avg'], label='60-Day Moving Average', color='red') plt.xlabel('Date') plt.ylabel('Wind Direction (degrees)') plt.title('60-Day Moving Average of Wind Direction') plt.legend() plt.show()

# Define the sector boundaries sectors = { 'N': (337.5, 22.5), 'NE': (22.5, 67.5), 'E': (67.5, 112.5), 'SE': (112.5, 157.5), 'S': (157.5, 202.5), 'SW': (202.5, 247.5), 'W': (247.5, 292.5), 'NW': (292.5, 337.5) } # Function to categorize each wind direction into a sector def categorize_direction(degrees): for sector, (lower, upper) in sectors.items(): if lower < upper: if lower <= degrees <= upper: return sector else: # Overlap case for North sector if degrees >= lower or degrees <= upper: return sector return 'Unknown' # Apply the categorization function to the wind direction data df['sector'] = df['winddir'].apply(categorize_direction) # Group by month and sector and count the occurrences df['month'] = df['datetime'].dt.month monthly_sector_counts = df.groupby(['month', 'sector']).size().unstack(fill_value=0) # Plot the sector frequency diagram for each month months = df['month'].unique() for month in months: plt.figure(figsize=(10, 6)) monthly_sector_counts.loc[month].plot(kind='bar') plt.title(f'Wind Direction Sector Frequency for Month {month:02d}') plt.xlabel('Wind Direction Sector') plt.ylabel('Frequency') plt.xticks(rotation=45) plt.tight_layout() plt.show()