cpstress 0.2.1
Cryptographically secure stress testing data generator library for competitive programming. Generates random test cases for arrays, numbers, strings, and graphs using secrets module.
Requires Python: >=3.7
Keywords: competitive programming test data generation random stress-testing

# cpstress

A comprehensive stress testing data generator for competitive programming 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
```bash
pip install cpstress
```

### From Source
```bash
git clone https://github.com/sharpsalt/cpstress.git
cd cpstress
pip install -e .
```

## Quick Start

```python
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](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

Srijan Verma - [GitHub Profile](https://github.com/sharpsalt)