Inner Resilience and Well-being
Description
In the realm of mathematics and artistry, a code dances with abstract concepts, translating them into vibrant visualisations and statistical insights. Let us embark on an insightful journey through this Python code, where mathematical functions become brushstrokes, creating portraits of inner resilience, detachment, equanimity, and more. Setting the Stage: The Canvas of X-Axis The canvas is laid out with precision, the x-axis stretching from 0 to 1, a landscape ready to host the mathematical expressions that will soon adorn it. Brushstroke 1: Inner Resilience The first stroke introduces us to the sinuous elegance of the sine function, a curve embodying the concept of inner resilience. As the x-values sway, the y-values ripple with strength and flexibility, capturing the essence of resilience in mathematical form. Brushstroke 2: Detachment from Outcomes The canvas transforms with the stroke of detachment. A symphony of mathematical calculations produces a line that doesn't cling to the axis of success or failure. It's a portrayal of detachment, where the distance from predetermined outcomes becomes a form of artistic expression. Brushstroke 3: Embracing Equanimity The cosine function takes centre stage, crafting a wave of equanimity. As x evolves, the y-values oscillate, reflecting the cyclical nature of life's dualities. This is not just a curve; it's a mathematical embrace of equanimity in the face of undulating circumstances. Brushstroke 4: Modern Yoga A more intricate dance unfolds with the sin function's higher frequency, illustrating a modern application of yoga. It's not just a physical pose; it's a rhythm of balance reverberating through the mathematical fabric, inviting us to synchronize with life's complexities. Brushstroke 5: Navigating Work and Life A linear equation joins the composition, a straight line cutting through the mathematical space. This is the brushstroke of navigating work and life with equipoise, a visual guide for maintaining balance amidst responsibilities. Brushstroke 6: Stress Reduction and Well-being The canvas is now touched by an exponential function, crafting an ethereal representation of stress reduction and well-being. As x meanders, the curve descends, symbolising the calming descent into a state of emotional well-being. Harmony in Diversity: The Symphony of Statistical Insights The visual feast doesn't stop at aesthetics. Statistical analyses are interwoven, revealing nuances in each stroke. Mean and standard deviation speak of central tendencies and variations. Skewness and kurtosis, akin to an art critic's gaze, unveil the shapes and twists of these mathematical portraits. Minimum and maximum values, the quiet observers, mark the boundaries of this artistic landscape.
Files
Steps to reproduce
import numpy as np import matplotlib.pyplot as plt from scipy.stats import describe, skew, kurtosis, linregress # Create a range for the x-axis x = np.linspace(0, 1, 100) # Function to represent Inner Resilience in Action inner_resilience = np.sin(2 * np.pi * x) # Function to represent Detachment from Outcomes detachment = 1 - np.abs(2 * x - 1) # Function to represent Embracing Equanimity equanimity = np.cos(2 * np.pi * x) # Function to represent Modern Application of Yoga modern_yoga = np.sin(4 * np.pi * x) # Function to represent Navigating Work and Life inner_equipoise = 0.5 * x + 0.25 # Function to represent Stress Reduction and Well-being well_being = np.exp(-8 * (x - 0.5)**2) # List of functions and labels functions = [inner_resilience, detachment, equanimity, modern_yoga, inner_equipoise, well_being] labels = ['Inner Resilience', 'Detachment', 'Equanimity', 'Modern Yoga', 'Inner Equipoise', 'Well-being'] # Plotting plt.figure(figsize=(14, 16)) for i in range(6): plt.subplot(3, 2, i + 1) plt.plot(x, functions[i], label=labels[i]) plt.title(labels[i]) # Statistical analysis stat_desc = describe(functions[i]) mean, std_dev = stat_desc.mean, np.sqrt(stat_desc.variance) skewness = skew(functions[i]) kurt = kurtosis(functions[i]) plt.text(0.5, 0.1, f'Mean: {mean:.2f}\nStd Dev: {std_dev:.2f}\nSkewness: {skewness:.2f}\nKurtosis: {kurt:.2f}', transform=plt.gca().transAxes, fontsize=12, verticalalignment='bottom', horizontalalignment='center') # Linear regression analysis slope, intercept, r_value, _, _ = linregress(x, functions[i]) plt.plot(x, slope * x + intercept, color='red', linestyle='dashed', label=f'Linear Regression (R={r_value:.2f})') plt.tight_layout() plt.show()