cpstress

A comprehensive stress testing data generator with cryptographically secure randomness.

Version History

v0.2.0 - Bug Fixes & Improvements

• Fixed edge cases in graph generation (weighted graph parameter handling)
• Improved performance for large-scale random generation
• Enhanced documentation and examples
• Fixed import paths and module organization
• Added better error handling for invalid parameters
• Optimized random number generation for cryptographic security

v0.1.0 - Initial Release

• Initial stable release with core functionality

Features

• Arrays: Random arrays, permutations, matrices, pairs, subsets, partitions, and specialized patterns (strictly increasing/decreasing, arithmetic/geometric progressions)
• Numbers: Random integers, floats, weighted random selection, exclusion sets
• Strings: Random strings with case control, palindromes, alphanumeric strings, custom alphabets
• Graphs: Trees, simple graphs, weighted graphs, directed graphs, DAGs, bipartite graphs, cycles, complete graphs, regular graphs
• Secure Randomness: Uses secrets.SystemRandom() instead of Python's predictable MT19937 algorithm

Installation

From PyPI

pip install cpstress

From Source

git clone https://github.com/sharpsalt/cpstress.git
cd cpstress
pip install -e .

Quick Start

from cpstress.generators import gen_arrays, gen_numbers, gen_strings, gen_graphs

# Generate random array
arr = gen_arrays.random(10, 1, 100)
print(arr)

# Generate random integer
num = gen_numbers.random_int(1, 1000)
print(num)

# Generate random string
s = gen_strings.random(15, CaseType.Mixed)
print(s)

# Generate tree
tree = gen_graphs.tree(10)
print(tree)

Module Reference

Arrays (gen_arrays)

• random(len, l, r, unique=False, sorted=False) - Random array
• permutation(n) - Random permutation
• matrix(rows, cols, l, r) - Random matrix
• pairs(len, l1, r1, l2, r2, ordered=False) - Random pairs
• subset(l, r, k, sorted=False) - Random subset
• partition(sum, k, min_val, max_val) - Partition sum into k parts
• strictly_increasing/decreasing() - Monotonic sequences
• arithmetic_progression/geometric_progression() - Special sequences
• bit_array(len, prob_one=0.5) - Binary array

Numbers (gen_numbers)

• random_int(l, r) - Random integer
• random_real(l, r) - Random float
• random_range(l, r, count) - List of random numbers
• random_exclude(l, r, exclude_set) - Random excluding values
• random_weighted(values, weights) - Weighted random selection
• random_real_exclude() - Random float with exclusion range

Strings (gen_strings)

• random(len, case_type) - Random string
• palindrome(len, case_type) - Random palindrome
• random_alphanum(len, letters, digits, case_type) - Alphanumeric string
• random_custom(len, alphabet) - String from custom alphabet
• random_strings(count, len, case_type) - Multiple random strings
• palindromes(count, len, case_type) - Multiple palindromes

Graphs (gen_graphs)

• tree(n, zero_based=False) - Random tree
• simple_graph(n, m) - Random undirected graph
• weighted_graph(n, m, min_w, max_w) - Weighted graph
• directed_graph(n, m) - Directed graph
• dag(n, m) - Directed acyclic graph
• bipartite(n1, n2, m) - Bipartite graph
• cycle(n), star(n), complete(n), regular(n, d) - Special graphs

Security

All random number generation uses Python's secrets.SystemRandom(), which provides cryptographically strong randomness that cannot be easily determined or predicted, unlike the standard random.Random() module.

License

This project is licensed under the MIT License - see LICENSE file for details.

Contributing

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

Author

Srijan Verma - GitHub Profile