Metadata-Version: 2.4
Name: datamaths
Version: 0.1.0
Summary: Reference code snippets for matrix operations, calculus, and optimization algorithms
Author-email: Lord Megatron <aryanjbagwe@gmail.com>
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: sympy
Requires-Dist: scipy

# datamaths

A pip-installable package built from `OMDS.py`. Each function prints out a
ready-to-run code snippet (matrix operations, differentiation/integration,
Newton's method, Lagrange multipliers, KKT conditions, BFGS, PSO, Flower
Pollination Algorithm, etc.) by raising it as a `RuntimeError`, so you can
copy it straight from the traceback.

## Install (from this folder)

```bash
pip install .
```

Or, for editable/development install:

```bash
pip install -e .
```

## Usage

```python
import datamaths

datamaths.commands()   # list all available snippet functions
datamaths.p1Matrixopr()  # raises RuntimeError containing the matrix-ops snippet
```

## Available functions

- `p1Matrixopr()`
- `p2vectorInte()`
- `p3simplexDuality()`
- `p4newton()`
- `p5secant()`
- `p6langrange()`
- `p7kkt()`
- `p8bfgs()`
- `p9swarm()`
- `p10flower()`
- `alllib()` – prints required third-party libraries and pip install commands
- `commands()` – lists all function names
