Release v1.0.0

Release v1.0.0 - Production Release 🎉

Release Date: July 15, 2021

🚀 Production-Ready Release

This is the first stable, production-ready release of Performance Profiler & Optimizer, featuring all planned core capabilities.

✨ What's New in v1.0.0

📈 Historical Performance Tracking

• SQLite database backend for long-term storage
• Automatic performance trend analysis
• Linear regression for performance trends
• Optimization recording and history
• Comprehensive reporting system
• Database persistence across sessions

🎯 Complete Feature Set

This release completes the original roadmap with:

1. 💾 Memory Profiling - Track memory usage and allocations
2. 🔍 Line-by-Line Analysis - Detailed per-line performance metrics
3. 💡 Auto-Optimization - AI-powered code improvement suggestions
4. 📊 Before/After Comparisons - Validate optimization improvements
5. 🎯 Bottleneck Detection - Identify and prioritize performance issues
6. 📈 Historical Tracking - Monitor performance trends over time

📦 Installation

pip install performance-profiler-optimizer

🚀 Quick Start

from performance_profiler import HistoricalTracker

tracker = HistoricalTracker(db_path="performance.db")

@tracker.track
def my_function():
    return sum(range(100000))

# Run multiple times to build history
for _ in range(10):
    my_function()

# View trends
tracker.print_history("my_function")
trend = tracker.get_trend("my_function", "execution_time")
print(f"Trend: {trend['trend']}")

📊 Example Output

📈 Performance History: my_function
=====================================
Timestamp            Time (s)     Memory (MB)     Peak (MB)
-------------------------------------------------------------
2021-07-15 10:30:00  0.002456     2.34           3.12
2021-07-15 10:30:01  0.002398     2.31           3.08
2021-07-15 10:30:02  0.002512     2.36           3.15

📊 Trend Analysis:
  Average Time: 0.002455s
  Min: 0.002398s, Max: 0.002512s
  Trend: STABLE
=====================================

🎓 Documentation

• README
• Examples
• CHANGELOG

📈 Project Statistics

• 2,610+ lines of code
• 7 core modules
• 7 comprehensive examples
• 18 months of development (Jan 2020 - Jul 2021)

🙏 Thank You

Thank you to everyone who contributed ideas and feedback during the development of this project!

---

Full Changelog: v0.6.0...v1.0.0

Release v0.6.0

Full Changelog: v0.5.0...v0.6.0

Release v0.5.0

Full Changelog: v0.4.0...v0.5.0

Release v0.4.0

Full Changelog: v0.3.0...v0.4.0

Release v0.3.0

Full Changelog: v0.2.0...v0.3.0

Release v0.2.0

Release v0.2.0 - Memory Profiling

Release Date: April 15, 2020

💾 Memory Profiling

This release adds comprehensive memory tracking capabilities to the profiler.

✨ New Features

Memory Profiler

• 💾 Real-time memory usage tracking with psutil
• 📊 Peak memory detection using tracemalloc
• 📈 Memory change monitoring per function call
• 🔍 System memory information retrieval
• 📉 Average memory consumption tracking

API Enhancements

• MemoryProfiler class
• profile_memory decorator
• get_memory_info() for system stats
• Visual memory usage reporting

🔧 Improvements

• Enhanced profiling results display
• Better memory statistics formatting
• Process memory isolation

📦 Installation

pip install performance-profiler-optimizer==0.2.0

🚀 Usage Example

from performance_profiler import MemoryProfiler

memory_profiler = MemoryProfiler()
memory_profiler.enable()

@memory_profiler.profile_memory
def memory_intensive():
    return [i**2 for i in range(1000000)]

memory_intensive()
memory_profiler.print_results()

📊 Example Output

💾 Memory Profiling Results
========================================
Function: memory_intensive
  Calls: 3
  Total Memory Allocated: 38.45 MB
  Peak Memory: 45.23 MB
  Average Memory per Call: 12.82 MB
========================================

---

Full Changelog: v0.1.0...v0.2.0

Release v0.1.0

Release v0.1.0 - Initial Project Structure

Release Date: January 15, 2020

🎉 First Release

This is the initial release of Performance Profiler & Optimizer, establishing the foundation for comprehensive performance analysis.

✨ Features

Core Profiler

• ⏱️ Basic execution time profiling
• 📊 Function call statistics (min, max, average)
• 🎯 Decorator-based profiling API
• 📈 Simple results display

Components Added

• PerformanceProfiler class
• Profile decorator for function instrumentation
• Statistics collection and reporting
• Clean API design for future expansion

📦 Installation

pip install performance-profiler-optimizer==0.1.0

🚀 Quick Start

from performance_profiler import PerformanceProfiler

profiler = PerformanceProfiler()

@profiler.profile
def my_function():
    return sum(range(1000000))

my_function()
profiler.print_results()

📝 Project Setup

• Created package structure
• Added setup.py and requirements
• Documented basic usage
• Established coding standards

🔮 Coming Soon

• Memory profiling
• Line-by-line analysis
• Optimization suggestions
• And more!

---

Full Changelog: https://github.com/officialgabzz/memory_profiler_optimiser/commits/v0.1.0