# TimeSpaceX
A powerful Python Time and Space Complexity Analyzer that helps you understand the computational complexity of your code.
## Features
- Analyzes both time and space complexity
- Provides detailed explanations
- Detects common patterns:
- Simple loops (O(n))
- Nested loops (O(n²), O(n³))
- Binary search patterns (O(log n))
- Divide and conquer algorithms (O(n log n))
- Recursive functions
- Matrix operations
- Beautiful command-line interface with syntax highlighting
## Installation
```bash
pip install timespacex
```
## Usage
Analyze a Python file:
```bash
timespacex your_file.py
```
Options:
```bash
timespacex --no-color your_file.py # Disable colored output
```
## Example
Given a Python file `example.py` with the following content:
```python
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
```
Running:
```bash
timespacex example.py
```
Will output:
```
Time & Space Complexity Analysis
==================================================
┌─ Function: binary_search ──────────────────────┐
│ The function `binary_search` has a time │
│ complexity of O(log n). This is because the │
│ function uses a binary search pattern, │
│ dividing the search space in half at each │
│ step. │
│ │
│ The space complexity is O(1). This is because │
│ the function uses a constant amount of extra │
│ space regardless of input size. │
└───────────────────────────────────────────────┘
```
## Limitations
- The analysis is based on static code analysis and may not catch all edge cases
- Complex algorithmic patterns might not be accurately detected
- The tool provides simplified complexity analysis and may not catch subtle optimizations
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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