15 Feb 2026

Python Setup

Complete workflow to install Python using pyenv and run SymPy-based symbolic algebra along with SciPy numerical integration.

macos python sympy scipy numerical physics

To do symbolic calculation safely and high-quality numerical work, the most practical stack is:

• version management → pyenv
• symbolic math → SymPy
• numerics → NumPy, SciPy

Below is the complete pipeline: install → create environment → install libraries → run tests.

Step 1 — Install pyenv (Python version manager)

    brew install pyenv

Add to shell:

export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

• First line creates an environment variable named PYENV_ROOT. Other commands use this value to know where pyenv files, versions, and shims are stored. Without it pyenv may not know its own home directory.

• Second line adds pyenv’s bin directory to the PATH, allowing you to run pyenv commands from the terminal.

• It runs pyenv’s initialization script every time the terminal starts. Therefore the eval line initializes pyenv, enabling it to manage Python versions and shims properly. This setup allows you to easily switch between different Python versions for various projects.

Step 2 — Install Python version

Example (stable for scientific work):

    pyenv install 3.11.7

Set global:

    pyenv global 3.11.7
    python --version

Step 3 — Create project environment (recommended)

    mkdir susy-project
    cd susy-project
    pyenv local 3.11.7

Step 4 — Upgrade pip

    pip install --upgrade pip

Step 5 — Install required libraries

    pip install sympy numpy scipy

Step 6 — Test Symbolic Computation (Supersymmetry style)

Create file:

    touch symbolic_test.py

Example: anticommuting symbols, algebra, simplification

from sympy import symbols, expand

# define operators
A, B = symbols('A B', commutative=False)

expr = (A + B)**2

print("Original expression:")
print(expr)

print("\nExpanded expression:")
print(expand(expr))

Run:

python symbolic_test.py

Step 7 — Test Numerical Integration

Create file:

touch numeric_test.py

Example: integrate sin(x) from 0 to pi

import numpy as np
from scipy import integrate

f = lambda x: np.sin(x)

result, error = integrate.quad(f, 0, np.pi)

print("Integral:", result)
print("Estimated error:", error)

Run:

python numeric_test.py

Expected ≈ 2.

Mixed Symbolic-Numeric Example

from sympy import symbols, sin, lambdify
import numpy as np
from scipy.integrate import quad

x = symbols('x')
expr = sin(x)

f = lambdify(x, expr, 'numpy')

res, _ = quad(f, 0, np.pi)
print(res)

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Running in Visual Notebook Environment (Mathematica-like Experience)

For a more interactive experience, you can use Jupyter Notebook or JupyterLab. They allow you to run code in cells and see outputs immediately, which is great for experimentation and learning.

pip install jupyterlab notebook

Jupyter provides two interfaces for running interactive Python code:

Jupyter Notebook

• Classic, simple interface
• Lightweight and minimal
• Cell-based execution
• Suitable for quick experiments and teaching

Run with: jupyter notebook

JupyterLab

• Modern, advanced interface
• Multiple tabs (notebooks, terminals, files)
• Integrated file browser
• More suitable for research and large projects

Run with: jupyter lab and if error occurs, try python -m jupyter lab which ensures it uses the correct Python environment.

Both use the same Python kernel and execute code identically. The difference is only in the user interface.