Emotional and Psychological impacts of stigma attached to Menstruation
Description
Key Elements: 1. Simulated Data: The code generates simulated emotional impact values categorized into 'Low,' 'Moderate,' and 'High' levels, reflecting the complexity and diversity of emotional responses. 2. Visual Trends: A line plot is created to visualize the trends in emotional impact values across 250 samples. The use of distinctive colours and markers enhances clarity in understanding the fluctuations in impact levels. 3. Impact Level Differentiation: The code employs scatter plots to highlight specific impact levels ('Low,' 'Moderate,' 'High') within the dataset. This allows for a nuanced examination of individual data points and their corresponding impact levels. 4. Customization: The plot is customized with informative labels, including a title indicating the subject ('Emotional and Psychological Impact Over 250 Samples'), axis labels, and specific tick marks for better interpretability. 5. Legend and Grid: A legend is included to identify the different impact levels, aiding viewers in associating colours with specific categories. The addition of a grid facilitates a more precise examination of data points. 6. Visual Output: The resulting plot is displayed, providing a visual snapshot of the simulated emotional impact trends. The graphical representation aids in discerning patterns, variations, and tendencies within the dataset.
Files
Steps to reproduce
import matplotlib.pyplot as plt import numpy as np def draw_emotional_impact_visualization(): # Simulated Data for Emotional and Psychological Impact levels = ['Low', 'Moderate', 'High'] impact_values = np.random.choice([0.2, 0.5, 0.8], size=250, p=[0.3, 0.4, 0.3]) # Simulated impact values # Create a line plot to visualize trends plt.figure(figsize=(10, 6)) plt.plot(impact_values, label='Emotional Impact', color='purple', marker='o', linestyle='-', markersize=5) # Highlight different impact levels with different colors for i, level in enumerate(levels): indices = np.where(impact_values == i)[0] plt.scatter(indices, impact_values[indices], label=f'{level} Impact', s=50) # Customize plot plt.title('Emotional and Psychological Impact Over 250 Samples') plt.xlabel('Sample') plt.ylabel('Impact Level') plt.xticks(np.arange(0, 251, step=50)) plt.yticks([0.2, 0.5, 0.8], labels=['Low', 'Moderate', 'High']) plt.legend() plt.grid(True) # Show the plot plt.show() # Call the function to draw the emotional impact visualization draw_emotional_impact_visualization()