Research Gap Types

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Imitating Graph With Claude

How to make graph like this in Python

I saw a nice graph from posting in https://x.com/junwatu/status/1851779704510124178/photo/1

Let’s ask Claude to imitate the graph.

I’ll help you create a similar line graph using Python, primarily using matplotlib with a modern, dark theme that matches the style of the image.

This code creates a graph similar to the one shown in your image. Here are the key features:

  1. Uses a dark theme matching GitHub’s dark mode
  2. Creates smooth lines with dots at data points
  3. Places country labels at the end of each line
  4. Uses custom colors for each country
  5. Includes a grid for better readability
  6. Removes chart borders for a modern look
  7. Reverses the y-axis to show rank 1 at the top
import matplotlib.pyplot as plt
import numpy as np

# Sample data
countries = ['India', 'United States', 'China', 'Brazil', 'United Kingdom',
             'Indonesia', 'Japan', 'Germany', 'Russia', 'Canada']

# Create sample data for demonstration
# In reality, you would have your actual data here
years = np.array([2024, 2025, 2026, 2027, 2028])
data = np.array([
    [1, 1, 1, 2, 2],  # India
    [2, 2, 2, 1, 1],  # United States
    [3, 3, 3, 3, 3],  # China
    [4, 4, 4, 4, 4],  # Brazil
    [5, 5, 5, 5, 5],  # UK
    [8, 6, 6, 6, 6],  # Indonesia
    [7, 7, 7, 7, 7],  # Japan
    [9, 8, 8, 8, 8],  # Germany
    [6, 9, 9, 9, 9],  # Russia
    [10, 10, 10, 10, 10]  # Canada
])

# Set the style
plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 8))

# Custom colors for each line
colors = ['#FF69B4', '#9370DB', '#87CEEB', '#90EE90', '#FFD700',
          '#FF6347', '#DDA0DD', '#BA55D3', '#ADD8E6', '#98FB98']

# Plot each line
for i, (country, color) in enumerate(zip(countries, colors)):
    ax.plot(years, data[i], color=color, linewidth=2, marker='o', markersize=8)
    # Add country name at the end of each line
    ax.text(years[-1] + 0.1, data[i, -1], country, color=color, 
            verticalalignment='center', fontsize=10)

# Customize the plot
ax.set_ylim(10.5, 0.5)  # Reverse y-axis and add some padding
ax.set_xlim(2024, 2028.5)  # Add space for labels on the right

# Set title
plt.title('Projecting the top 10 developers communities\non GitHub 2024-2028', 
          fontsize=16, pad=20)

# Customize grid
ax.grid(True, linestyle='--', alpha=0.2)

# Customize axes
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

# Set y-axis ticks
ax.set_yticks(range(1, 11))

# Remove x-axis ticks but keep labels
ax.tick_params(axis='x', length=0)

# Set background color
fig.patch.set_facecolor('#0D1117')
ax.set_facecolor('#0D1117')

# Adjust layout
plt.tight_layout()

# Show plot
plt.show()

To use this code, you’ll need to have matplotlib installed. You can install it using:

pip install matplotlib

You can modify the data array to match your actual data points. Each row represents a country’s ranking over the years.

The Computer Language Benchmark Game in Papers

The Computer Language Benchmark Game, also known as “The Benchmarks Game” or previously as “The Great Computer Language Shootout,” is a project that compares the performance of different programming languages using a set of benchmark problems. It’s a valuable resource for researchers comparing language performance.

Here are a few papers from recent years that have utilized CLBG:

  1. “An Empirical Study on the Energy Consumption of Python Idioms” (2021) by Oliveira et al. This paper used CLBG to study the energy efficiency of different Python programming styles.
  2. “Performance and Energy Efficiency Across Programming Languages” (2021) by Pereira et al. This study used CLBG to compare the performance and energy consumption of various programming languages.
  3. “Automated Detection of Performance Regressions Using Statistical Learning Techniques” (2020) by Laaber et al. While not exclusively about CLBG, this paper used it as part of their methodology for detecting performance regressions in software.

Recent References on Programming Language Energy Efficiency

These papers cover various aspects of the topic, including:

  1. Ranking programming languages by energy efficiency
  2. Energy characteristics of specific APIs and languages (e.g., Java, Haskell)
  3. Energy patterns for mobile applications
  4. Manifestos and best practices for energy-aware software development
  5. Case studies and empirical evaluations of energy efficiency in software engineering
  6. Tools and techniques for estimating and measuring software energy consumption
  7. Practitioners’ perspectives on green software engineering
  8. Comparisons of energy consumption in different programming contexts

Recent References on Energy Efficiency in Programming Languages (Last 5 Years)

  1. Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., & Saraiva, J. (2021). Ranking Programming Languages by Energy Efficiency. https://www.sciencedirect.com/science/article/abs/pii/S0167642321000022 .
  2. Zakaria Ournani, Evaluating The Energy Consumption of Java I/O APIs https://ieeexplore.ieee.org/document/9609210
  3. Cruz, L., Abreu, R. Catalog of energy patterns for mobile applications. Empir Software Eng 24, 2209–2235 (2019). https://doi.org/10.1007/s10664-019-09682-0
  4. Fonseca, A., Kazman, R., & Lago, P. (2019). A manifesto for energy-aware software. https://ieeexplore.ieee.org/document/8880037
  5. Luís Gabriel Lima, Francisco Soares-Neto, Paulo Lieuthier, Fernando Castor, Gilberto Melfe, João Paulo Fernandes, On Haskell and energy efficiency,
    Journal of Systems and Software, https://doi.org/10.1016/j.jss.2018.12.014.
    (https://www.sciencedirect.com/science/article/pii/S0164121218302747)
  6. R. Verdecchia, G. Procaccianti, I. Malavolta, P. Lago and J. Koedijk, “Estimating Energy Impact of Software Releases and Deployment Strategies: The KPMG Case Study,” 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Toronto, ON, Canada, 2017, pp. 257-266, doi: 10.1109/ESEM.2017.39.
  7. Chowdhury, S., Borle, S., Romansky, S. et al. GreenScaler: training software energy models with automatic test generation. Empir Software Eng 24, 1649–1692 (2019). https://doi.org/10.1007/s10664-018-9640-7
  8. An empirical study of practitioners’ perspectives on green software engineering https://dl.acm.org/doi/10.1145/2884781.2884810
  9. Procaccianti, G., Fernández, H., & Lago, P. (2019). Empirical evaluation of two best practices for energy-efficient software development. Journal of Systems and Software, 147, 64-80. (https://research.vu.nl/en/publications/empirical-evaluation-of-two-best-practices-for-energy-efficient-s)
  10. Hamizi, I., Bakare, A., Fraz, K., Dlamini, G., Kholmatova, Z. (2021). A Meta-analytical Comparison of Energy Consumed by Two Different Programming Languages. In: Succi, G., Ciancarini, P., Kruglov, A. (eds) Frontiers in Software Engineering. ICFSE 2021. Communications in Computer and Information Science, vol 1523. Springer, Cham. https://doi.org/10.1007/978-3-030-93135-3_12