Iteration 2 analysis: Environmental Ethics
Description
Exploring Spiritual-Ecological Dynamics through Simulation and Statistical Analysis This Python code utilizes NumPy and Matplotlib to simulate and analyze the intricate interplay between spiritual, ecological, and theological elements over a designated time period. The simulation encompasses 100 iterations, representing a continuum from 0 to 1. The simulated data includes four distinct terms: "Soteriological Epistemology," "Preserve Ecological Diversity," "Name God from Creatures," and "Idolatry Threat." These terms are generated using random normal distributions, capturing the inherent variability in spiritual and ecological dynamics. The cumulative sum operation is applied to simulate the evolving nature of "Preserve Ecological Diversity," portraying a dynamic, ongoing process. Following the simulation, statistical analysis is performed, computing the mean, standard deviation, and correlation coefficients among the simulated attributes. The calculated statistical measures offer insights into the central tendencies and relationships within the dynamic system. The visual representation unfolds through a series of line plots, showcasing the temporal evolution of each term and their collective impact on the "Combined Equation Result." Each trajectory is labeled, providing a clear overview of their individual and collective contributions. This code serves as a tool for exploring and understanding the complex relationships between spirituality, ecological dynamics, and theological perspectives. The statistical analysis complements the visual representation, providing quantitative insights into the simulated attributes. The combined approach offers a comprehensive exploration of the intricate dynamics encapsulated in the simulated spiritual-ecological equation.
Files
Steps to reproduce
import numpy as np import matplotlib.pyplot as plt # Number of simulations num_simulations = 100 time = np.linspace(0, 1, num_simulations) # Assuming time varies from 0 to 1 # Generate simulated data for each term in the equation soteriological_epistemology = np.random.randn(num_simulations) preserve_ecological_diversity = np.cumsum(np.random.randn(num_simulations)) name_god_from_creatures = np.random.randn(num_simulations) idolatry_threat = np.random.randn(num_simulations) # Combine the terms equation_result = ( soteriological_epistemology + preserve_ecological_diversity + name_god_from_creatures + idolatry_threat ) # Statistical Analysis attributes = [soteriological_epistemology, preserve_ecological_diversity, name_god_from_creatures, idolatry_threat, equation_result] attribute_names = ['Soteriological Epistemology', 'Preserve Ecological Diversity', 'Name God from Creatures', 'Idolatry Threat', 'Combined Equation Result'] # Calculate mean, standard deviation, and correlation means = [np.mean(attr) for attr in attributes] std_devs = [np.std(attr) for attr in attributes] correlations = np.corrcoef(attributes) # Print statistical information for i in range(len(attributes)): print(f"{attribute_names[i]} - Mean: {means[i]}, Standard Deviation: {std_devs[i]}") # Plot the results plt.figure(figsize=(10, 6)) plt.plot(time, soteriological_epistemology, label='Soteriological Epistemology') plt.plot(time, preserve_ecological_diversity, label='Preserve Ecological Diversity') plt.plot(time, name_god_from_creatures, label='Name God from Creatures') plt.plot(time, idolatry_threat, label='Idolatry Threat') plt.plot(time, equation_result, label='Combined Equation Result', linestyle='--', linewidth=2) plt.title('Visual Representation of the Equation') plt.xlabel('Time') plt.ylabel('Values') plt.legend() plt.grid(True) plt.show()