kika-nd 0.1.1

A comprehensive Python toolkit for nuclear data analysis, including MCNP input/output processing, sensitivity analysis, covariance handling, ACE file parsing, and ENDF data manipulation.

KIKA

A comprehensive Python toolkit for nuclear data analysis, Monte Carlo simulation support, and uncertainty quantification. KIKA provides tools for working with MCNP, ENDF, ACE files, covariance matrices, and sensitivity analysis.

Features

MCNP Processing

• Parse and manipulate MCNP input files (materials, PERT cards)
• Read and analyze MCTAL output files
• Tally data extraction and visualization

Sensitivity Analysis

• Compute sensitivity data using PERT cards
• Generate and visualize sensitivity profiles
• Create Sensitivity Data Files (SDF) compatible with SCALE

Nuclear Data

• ACE: Parse ACE format nuclear data files
• ENDF: Read Evaluated Nuclear Data Files
• Covariance: Handle covariance matrices from SCALE and NJOY

Additional Tools

• Energy group structure definitions
• Serpent Monte Carlo code support
• Uncertainty quantification utilities

Installation

pip install kika-nd

For development features:

# Install with development dependencies
pip install kika-nd[dev]

# Install with documentation dependencies
pip install kika-nd[docs]

Quick Start

import kika

# Read an MCNP input file
input_data = kika.read_mcnp("path/to/input_file")

# Read a MCTAL file
mctal = kika.read_mctal("path/to/mctal_file")

# Access materials
materials = input_data.materials

# Compute sensitivity data
sens_data = kika.compute_sensitivity(
    inputfile="path/to/input_file",
    mctalfile="path/to/mctal_file", 
    tally=4, 
    nuclide=26056, 
    label='Sensitivity Fe-56'
)

# Read ACE data
ace_data = kika.read_ace("path/to/ace_file")

# Read covariance matrices
cov = kika.read_scale_covmat("path/to/covmat_file")

Documentation

For complete documentation, examples, and API reference, visit: KIKA Documentation

GUI Application

A standalone GUI application for KIKA is available at kika-app.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.