Ego Defence Mechanism and Resilience
Description
Relationship between Ego Defence Mechanisms and Resilience: The first subplot illustrates the relationship between Ego Defence Mechanisms (EDM) and Resilience. The x-axis represents the variation in Ego Defence Mechanisms (EDM), ranging from 0 to 1. The y-axis represents Resilience, calculated as 1 minus Ego Defence Mechanisms, with added random variations. Random variations are introduced for demonstration purposes, creating slight fluctuations in the Resilience values. The plot visually showcases how changes in Ego Defence Mechanisms influence Resilience. Incorporating Thomistic Virtue: The second subplot introduces Thomistic Virtue to the relationship between EDM and Resilience. An arbitrary value (V) representing Thomistic Virtue is incorporated into the Resilience calculation, alongside random variations. The plot demonstrates how Resilience is affected when Thomistic Virtue is considered along with Ego Defense Mechanisms. Factors Influencing Adaptation: The third subplot explores the influence of various factors (L, S, C) on Resilience. Coefficients (a, b, c) are assigned to each factor, and their weighted contributions are combined with random variations to calculate Resilience. Factors L, S, and C are linearly spaced values between 0 and 1. The plot provides insight into how Resilience is shaped by the interplay of these factors. Overall, the code generates a comprehensive set of visualisations, offering a nuanced understanding of Resilience concerning Ego Defence Mechanisms, Thomistic Virtue, and other influencing factors. The tight layout ensures clarity in presentation.
Files
Steps to reproduce
import matplotlib.pyplot as plt import numpy as np # Generate random variations for demonstration np.random.seed(42) random_variations = np.random.normal(0, 0.1, 100) # Relationship between Ego Defense Mechanisms (EDM) and Resilience E = np.linspace(0, 1, 100) R = 1 - E + random_variations # Incorporating Thomistic Virtue V = 0.5 # Arbitrary value representing Thomistic virtue R_virtue = V * R + random_variations # Factors Influencing Adaptation L = np.linspace(0, 1, 100) S = np.linspace(0, 1, 100) C = np.linspace(0, 1, 100) a, b, c = 0.3, 0.4, 0.5 # Arbitrary coefficients R_factors = a * L + b * S + c * C + random_variations # Plotting plt.figure(figsize=(15, 5)) # Subplot 1: Relationship between Ego Defense Mechanisms and Resilience plt.subplot(1, 3, 1) plt.plot(E, R, label='Resilience') plt.xlabel('Ego Defense Mechanisms (EDM)') plt.ylabel('Resilience') plt.title('Relationship between EDM and Resilience') plt.legend() # Subplot 2: Incorporating Thomistic Virtue plt.subplot(1, 3, 2) plt.plot(E, R_virtue, label='Resilience with Thomistic Virtue') plt.xlabel('Ego Defense Mechanisms (EDM)') plt.ylabel('Resilience with Thomistic Virtue') plt.title('Incorporating Thomistic Virtue') plt.legend() # Subplot 3: Factors Influencing Adaptation plt.subplot(1, 3, 3) plt.plot(L, R_factors, label='Resilience with Factors') plt.xlabel('Factors (L, S, C)') plt.ylabel('Resilience with Factors') plt.title('Factors Influencing Adaptation') plt.legend() plt.tight_layout() plt.show()