¶

Modern Optimization Visualization and Analysis

Comprehensive Python library for visualizing and analyzing single- and multi-objective optimization results.

Features¶

Rich Visualizations

Interactive Pareto fronts, parallel coordinates, scatter matrices, and more.

Plotting Guide
Advanced Analysis

Smart Pareto Filter, knee point detection, TOPSIS, and quality metrics.

Analysis Tools
Flexible Storage

Memory, filesystem, or database backends with caching and replication.

Storage Guide
Interactive Dashboard

Modular Dash application with auto-discovery and page templates.

Dash App
Format Agnostic

CSV, JSON, HDF5, and custom formats with automatic detection.

File Formats
Extensible API

Clean, type-safe API with comprehensive documentation.

API Reference

Quick Example¶

from optiscope import load_results
from optiscope.plotting import plot_pareto_front_2d
from optiscope.analysis import smart_pareto_filter, topsis_from_result

# Load optimization results
result = load_results("optimization_data.csv")

# Filter to representative Pareto points
pareto_indices = smart_pareto_filter(result.objectives, epsilon=0.15)
result.create_set("pareto", pareto_indices, "smart_filter")

# Visualize Pareto front
fig = plot_pareto_front_2d(
    result,
    obj_x="cost",
    obj_y="weight",
    pareto_set="pareto",
    show_ideal_nadir=True
)
fig.show()

# Rank solutions with TOPSIS
topsis_result = topsis_from_result(
    result,
    weights={"cost": 0.5, "weight": 0.3, "efficiency": 0.2}
)

print(f"Best solution: {topsis_result['ranks'][0]}")