AHU Static Pressure Distribution Curve

This example demonstrates how to chart AHU static pressure distribution for a time period by AHU. Load your data from BDXpy, select a timeframe and revise any variables or chart formatting.

Output

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    import pandas as pd
    import numpy as np
    import seaborn as sns
    import matplotlib.pyplot as plt

    # Parameters for dummy data
    np.random.seed(0)  # For reproducibility
    num_ahus = 5
    ahus = [f'AHU_{i+1}' for i in range(num_ahus)]
    date_range = pd.date_range(start='2023-10-01', end='2023-10-31', freq='H')
    num_samples = len(date_range)

    # Generate random static pressure data with different transformations for each AHU
    data = {
        'datetime': np.tile(date_range, num_ahus),
        'AHU': np.repeat(ahus, num_samples),
        'static_pressure': []
    }

    # Apply different transformations to each AHU for variation
    for i, ahu in enumerate(ahus):
        if i == 0:  # AHU 1 - Flat line with slight random variation
            pressures = np.random.uniform(1.5, 2.0, num_samples)
        elif i == 1:  # AHU 2 - Logarithmic increase
            pressures = np.logspace(0.1, 1, num_samples) / 10 + np.random.uniform(-0.1, 0.1, num_samples)
        elif i == 2:  # AHU 3 - Polynomial curve (quadratic)
            pressures = (np.linspace(1, 3, num_samples) ** 2) / 5 + np.random.uniform(-0.1, 0.1, num_samples)
        elif i == 3:  # AHU 4 - Polynomial curve (cubic)
            pressures = (np.linspace(1, 3, num_samples) ** 3) / 10 + np.random.uniform(-0.1, 0.1, num_samples)
        else:  # AHU 5 - Linear with slight random variation and top values increased
            pressures = np.linspace(1.0, 3.0, num_samples) + np.random.uniform(-0.1, 0.1, num_samples)
            top_5_percent = int(0.05 * num_samples)
            pressures[:top_5_percent] += np.linspace(0.5, 1.0, top_5_percent)  # Slight increase in top 5%

        data['static_pressure'].extend(pressures)

    # Create the DataFrame
    df = pd.DataFrame(data)

    # Plot the KDE curve using seaborn's displot
    sns.set(style="whitegrid")
    plt.figure(figsize=(10, 6))
    sns.displot(df, x='static_pressure', hue='AHU', kind="kde", fill=True, height=6, aspect=1.5)

    # Set title and adjust layout to avoid cutoff
    plt.suptitle("Curve of Static Pressure Ranges by AHU", y=1.05, fontsize=16)
    plt.xlabel("Static Pressure (in w.c.)")
    plt.ylabel("Density")
    plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adding padding for the title

    # Save the plot as a PNG file
    plt.savefig("AHU_static_pressure_kde_curve.png", bbox_inches="tight")
    plt.show()