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


def create_heatmap_plots(data, response_var, pivot_var1, pivot_var2, order1=None, order2=None):
    """
    Create a heatmap plot based on a list of data and given variables.

    :param data: List of dictionaries, input data.
    :param response_var: String, response variable to be plotted.
    :param pivot_var1: String, first pivot variable to be used in the plot.
    :param pivot_var2: String, second pivot variable to be used in the plot.
    :param order1: List, the order of categories for pivot_var1. Optional.
    :param order2: List, the order of categories for pivot_var2. Optional.

    Returns:
        None. Displays the generated plot.
    """

    # Create a DataFrame from your list of dictionaries
    df = pd.DataFrame(data)

    # Convert the response variable column to float type if it's not already
    df[response_var] = df[response_var].astype(float)

    # Create a pivot table
    pivot = df.pivot_table(index=pivot_var1, columns=pivot_var2, values=response_var)

    # If an order is provided, reorder the pivot table
    if order1 is not None:
        pivot = pivot.reindex(order1)
    if order2 is not None:
        pivot = pivot[order2]

    # Plot the heatmap
    plt.figure(figsize=(10, 6))
    sns.heatmap(pivot, annot=True, fmt=".2f", cmap='coolwarm')
    plt.title(f"Heatmap of {response_var} by {pivot_var1} and {pivot_var2}")
    plt.show()