Implementation:Mbzuai oryx Awesome LLM Post training Matplotlib Bar Chart

Knowledge Sources	Awesome-LLM-Post-training Matplotlib pyplot
Domains	Visualization, Bibliometrics
Last Updated	2026-02-08 07:30 GMT

Overview

Concrete tool for generating publication trend bar charts using matplotlib in the research trend analysis pipeline.

Description

The visualization block in future_research_data.py creates a bar chart for each research keyword using plt.bar, with years on the x-axis and paper counts on the y-axis. Charts are styled with a fixed color (#2c7fb8), 12x6 figure dimensions, labeled axes, gridlines on the y-axis, and value labels on top of each bar for precise reading. Each chart is saved as a PNG file in the results/ directory and displayed via plt.show().

Usage

This visualization runs once per keyword in the main processing loop, after the yearly publication counts have been collected. It requires a pandas DataFrame with Year and Papers Published columns.

Code Reference

Source Location

• Repository: Awesome-LLM-Post-training
• File: scripts/future_research_data.py
• Lines: 71-91

Signature

# Inline visualization block (not a standalone function)
plt.figure(figsize=(12, 6))
bars = plt.bar(df_counts['Year'], df_counts['Papers Published'], color='#2c7fb8')
plt.title(f'Publication Trend for "{keyword}" ({category})', fontsize=14, pad=20)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Papers', fontsize=12)
plt.xticks(years)
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Add value labels on top of each bar
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:,}', ha='center', va='bottom')

plt.tight_layout()
figure_filename = os.path.join(output_dir, f"Publication_Trend_{keyword.replace(' ', '_')}.png")
plt.savefig(figure_filename)
plt.show()

Import

import os
import matplotlib.pyplot as plt
import pandas as pd

I/O Contract

Inputs

Name	Type	Required	Description
df_counts	pandas.DataFrame	Yes	DataFrame with 'Year' and 'Papers Published' columns
keyword	str	Yes	Research keyword (used in chart title and filename)
category	str	Yes	Category name (used in chart title)
years	list[int]	Yes	List of years for x-axis ticks
output_dir	str	Yes	Directory path for saving PNG files

Outputs

Name	Type	Description
PNG file	File	Bar chart image saved to results/Publication_Trend_{keyword}.png
Display	Plot	Chart displayed via plt.show() during execution

Usage Examples

Generate a Trend Bar Chart

import os
import matplotlib.pyplot as plt
import pandas as pd

# Sample data for one keyword
keyword = "RLHF"
category = "Reinforcement Learning"
years = [2020, 2021, 2022, 2023, 2024, 2025]
counts = [50, 120, 340, 890, 1500, 2100]
output_dir = "results"
os.makedirs(output_dir, exist_ok=True)

# Create DataFrame
df_counts = pd.DataFrame({"Year": years, "Papers Published": counts})

# Generate bar chart
plt.figure(figsize=(12, 6))
bars = plt.bar(df_counts['Year'], df_counts['Papers Published'], color='#2c7fb8')
plt.title(f'Publication Trend for "{keyword}" ({category})', fontsize=14, pad=20)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Papers', fontsize=12)
plt.xticks(years)
plt.grid(axis='y', linestyle='--', alpha=0.7)

for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:,}', ha='center', va='bottom')

plt.tight_layout()
figure_filename = os.path.join(output_dir, f"Publication_Trend_{keyword.replace(' ', '_')}.png")
plt.savefig(figure_filename)
plt.show()

Related Pages

Implements Principle

• Principle:Mbzuai_oryx_Awesome_LLM_Post_training_Trend_Visualization

Requires Environment

• Environment:Mbzuai_oryx_Awesome_LLM_Post_training_Python_Matplotlib