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Performance Optimization Guide

Welcome to the MONET Plots performance optimization guide! This comprehensive resource will help you create efficient, fast, and scalable visualizations, especially when working with large datasets and complex plotting workflows.

Overview

MONET Plots is designed with performance in mind, but large datasets and complex visualizations can still present challenges. This guide provides strategies and techniques to optimize your plotting performance.

Optimization Level Difficulty Focus Area
Basic Optimization Beginner Quick wins for most users
Memory Management Intermediate Large datasets and memory usage
Rendering Optimization Advanced Plot generation speed
Workflow Optimization Intermediate Multi-plot workflows

Performance Principles

1. Downsample When Possible

Reduce data points for interactive viewing while preserving important features.

2. Close Plots Properly

Always close plots when done to free memory resources.

3. Use Appropriate Data Types

Choose the right data structure for your use case.

4. Batch Operations

Group similar operations together for efficiency.

5. Cache Results

Save intermediate results to avoid recomputation.

Quick Start: Basic Optimization

import numpy as np
from monet_plots import SpatialPlot

# Before: Full resolution
large_data = np.random.random((1000, 1500))  # 1.5M points
plot = SpatialPlot()
plot.plot(large_data)  # Slow!

# After: Downsampled
small_data = large_data[::10, ::10]  # 15K points
plot = SpatialPlot()
plot.plot(small_data)  # Much faster!

Performance Metrics

Monitor these key performance indicators:

  • Generation Time: Time to create the plot
  • Memory Usage: RAM consumed during plotting
  • File Size: Output file size
  • Rendering Speed: Time to display or save
  • Interactive Responsiveness: Performance in interactive environments