# Literacy Improvement Model

Published: 27 March 2024| Version 1 | DOI: 10.17632/hr2g2p37rv.1
Contributor:
Sunil Maria Benedict

## Description

This dataset represents a simulated literacy improvement model over a period of time, visualized through a plot. Here's a breakdown of the data and its visualization: Literacy Improvement Model Functions: Two functions are defined to model literacy rates over time: L(t): Represents the literacy rate before interventions. It is defined as a sine wave with an added constant (50) to shift it up. L_prime(t): Represents the literacy rate after interventions. It is also defined as a sine wave with a slightly higher constant (52) to simulate improvement. Time Range: The time range t spans from 0 to 10 years, divided into 100 equally spaced intervals. This time range represents the duration over which the literacy rates are observed. Literacy Rates Before and After Interventions: The literacy rates before and after interventions are calculated using the defined functions L(t) and L_prime(t). These rates are plotted against time. The plot visually demonstrates the change in literacy rates over the specified time period. Total Improvement: The total improvement in literacy rates resulting from the interventions is calculated by computing the area under the curve of the difference between literacy rates after and before interventions. This total improvement is annotated on the plot to provide a quantitative measure of the effectiveness of the interventions. Visual Representation: The plot visualizes the literacy rates over time, with the literacy rate before interventions plotted in blue and the rate after interventions plotted in orange. The area between the two curves is shaded, with green indicating improvement and red indicating decline. Annotations provide additional information, such as the total improvement in literacy rates. Overall, this dataset and its visualization offer insights into the simulated impact of interventions on literacy rates over time, providing a visual and quantitative analysis of the effectiveness of the interventions.

## Steps to reproduce

import matplotlib.pyplot as plt import numpy as np # Define the literacy improvement model def L(t): return np.sin(t) + 50 def L_prime(t): return np.sin(t) + 52 # Time range t = np.linspace(0, 10, 100) # Literacy rates before and after interventions before_intervention = L(t) after_intervention = L_prime(t) # Calculate total improvement total_improvement = np.trapz(after_intervention - before_intervention, t) # Plot the literacy rates plt.figure(figsize=(10, 6)) plt.plot(t, before_intervention, label='Before Interventions') plt.plot(t, after_intervention, label='After Interventions') plt.fill_between(t, before_intervention, after_intervention, where=(after_intervention >= before_intervention), interpolate=True, color='green', alpha=0.3) plt.fill_between(t, before_intervention, after_intervention, where=(after_intervention < before_intervention), interpolate=True, color='red', alpha=0.3) plt.xlabel('Time (years)') plt.ylabel('Literacy Rate') plt.title('Literacy Improvement Model') plt.legend() # Annotate total improvement plt.text(5, 50, f'Total Improvement: {total_improvement:.2f}', fontsize=12, bbox=dict(facecolor='white', alpha=0.5)) plt.grid(True) plt.show()

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