Health Impact model - before and after intervention
Description
This Python code utilizes Matplotlib to visualize the impact of an intervention on health outcomes over time. Here's a breakdown of the code: Importing Libraries: The code begins by importing Matplotlib as plt and NumPy as np. Defining Health Outcome Functions: Two functions, H(t) and H_prime(t), are defined to model the health outcomes before and after the intervention, respectively. These functions represent how health outcomes change over time. Defining Time Range: The time range is defined using np.linspace() to create an array of evenly spaced values from 0 to 10, representing the time span over which the health outcomes are observed. Creating the Plot: A new figure with a specified size is created using plt.figure(). The health outcomes before and after the intervention are plotted on the same graph using plt.plot(). Different line styles (dashed for before intervention and solid for after intervention) and colors (blue and green) are used to distinguish between the two sets of data. Adding Significant Points: Marker points are added using plt.scatter() to indicate significant points in the data. These points represent critical points before and after the intervention. Adding Annotations: Annotations with arrows are added using plt.annotate() to provide additional information about the critical points. These annotations help clarify the impact of the intervention on health outcomes. Adding Labels and Title: Labels for the x-axis ('Time (years)') and y-axis ('Health Outcomes') are added using plt.xlabel() and plt.ylabel(), respectively. A title ('Health Impact Before and After Intervention') is added to describe the purpose of the plot. Adding Legend and Grid: A legend is added using plt.legend() to identify the different data series (before and after intervention). Grid lines are added using plt.grid(True) for better readability. Displaying the Plot: Finally, plt.show() is called to display the plot with all the added elements (lines, markers, annotations, labels, legend, and grid). Overall, this code visualizes the change in health outcomes before and after an intervention, providing insights into the effectiveness of the intervention over time.
Files
Steps to reproduce
import matplotlib.pyplot as plt import numpy as np # Health outcome functions def H(t): return 0.2 * t + 3 def H_prime(t): return 0.3 * t + 4 # Time range t = np.linspace(0, 10, 100) # Health outcomes over time plt.figure(figsize=(10, 6)) # Plotting health outcomes before and after intervention plt.plot(t, H(t), label='Before Intervention', linestyle='--', color='blue') plt.plot(t, H_prime(t), label='After Intervention', linestyle='-', color='green') # Adding markers to indicate significant points plt.scatter([5], [H(5)], color='red', label='Critical Point Before Intervention', zorder=5) plt.scatter([7], [H_prime(7)], color='orange', label='Critical Point After Intervention', zorder=5) # Adding annotations for critical points plt.annotate('Before Intervention', xy=(5, H(5)), xytext=(6, H(5) + 0.5), arrowprops=dict(facecolor='black', shrink=0.05), fontsize=10) plt.annotate('After Intervention', xy=(7, H_prime(7)), xytext=(8, H_prime(7) + 0.5), arrowprops=dict(facecolor='black', shrink=0.05), fontsize=10) plt.xlabel('Time (years)') plt.ylabel('Health Outcomes') plt.title('Health Impact Before and After Intervention') plt.legend() plt.grid(True) plt.show()