neuromath 0.1.2

NeuroMath - A lightweight symbolic mathematical DSL

NeuroMath

A custom mathematical and symbolic computation language built from scratch using Python.

NeuroMath is a mini computer algebra system (CAS) that supports:

✅ Variable assignments

✅ User-defined functions

✅ Symbolic differentiation

✅ Symbolic integration

✅ Mathematical built-in functions

✅ Notebook-style Streamlit interface

✅ Clean interpreter architecture (Lexer → Parser → AST → Interpreter)

🚀 Demo x = 5 f(x) = 3x^2 + 5x

print(f(2)) print(diff(f(x), x))

Output:

22 6*x + 5 📌 Features 🔹 Core Language Features

Arithmetic operations: +, -, *, /, ^

Variable assignment

Function definition

Function calls

Nested expressions

Multi-line programs

Print function

typeof() support

🔹 Symbolic Computation

Powered internally via SymPy conversion layer.

Supported symbolic operations:

diff(expression, variable)

integrate(expression, variable)

Symbolic simplification

Mixed numeric + symbolic expressions

Example:

f(x) = x^3 + 2*x diff(f(x), x)

Output:

3*x^2 + 2 🏗️ Architecture

NeuroMath is built using a traditional interpreter pipeline:

Source Code ↓ Lexer (Tokenization) ↓ Parser (AST Construction) ↓ Interpreter (Execution Engine) ↓ Symbolic Conversion Layer (SymPy) Components Module Responsibility lexer/ Converts source code into tokens parser/ Builds Abstract Syntax Tree ast/ AST node definitions interpreter/ Executes AST symbolic engine Converts AST to SymPy expressions streamlit app Notebook-style GUI 📚 Language Syntax Variables x = 10 y = x + 5

Rules:

Must start with a letter

Case-sensitive

Cannot start with a number

Function Definition f(x) = x^2 + 3*x

Call:

f(2) Symbolic Operations diff(x^2, x) integrate(x^2, x) Print print(x) Built-in Mathematical Functions

sin(x)

cos(x)

tan(x)

sqrt(x)

log(x)

exp(x)

pi

e

Example:

sin(pi/2) 🖥️ Notebook Interface

NeuroMath includes a Streamlit-powered notebook UI that mimics Wolfram / Jupyter-style execution:

Cell-based execution

Persistent environment

Reset kernel

Execution history

Run:

streamlit run app.py ⚙️ Installation

Clone repository:

git clone https://github.com/yourusername/neuromath.git cd neuromath

Install dependencies:

pip install -r requirements.txt

Run CLI:

python main.py

Run GUI:

streamlit run app.py 🧪 Example Programs Numeric Evaluation x = 5 y = 7 z = x*y + 3 print(z) Symbolic + Numeric Mixed x = 10 diff(x^2, x)

Output:

2*x

(Symbolic mode correctly ignores numeric memory.)

🔬 Design Highlights

Clean separation of numeric and symbolic evaluation modes

Context-aware AST to SymPy transformation

Local function scope isolation

Session-state safe Streamlit architecture

Modular and extensible design

📁 Project Structure neuromath/ │ ├── lexer/ ├── parser/ ├── ast/ ├── interpreter/ ├── app.py ├── main.py ├── requirements.txt └── README.md