PhysPlot

A Python library for creating professional physics plots with regression analysis and LaTeX formatting.

Features

• Professional LaTeX formatting for publication-ready plots
• Flexible regression analysis with any user-defined function
• Built-in common functions (linear, quadratic, exponential, Gaussian, etc.)
• Automatic R² calculation and parameter uncertainty estimation
• Multiple dataset comparison capabilities
• PDF output for high-quality plots

Installation

Install the required dependencies:

pip install -r requirements.txt

Note: LaTeX must be installed on your system for proper text rendering. On Ubuntu/Debian:

sudo apt-get install texlive-latex-base texlive-latex-extra texlive-fonts-recommended

Quick Start

from physplot import PhysPlot, quadratic
import numpy as np

# Initialize plotter
plotter = PhysPlot()

# Generate sample data
x = np.linspace(0, 10, 50)
y = quadratic(x, 2, -1, 1) + np.random.normal(0, 0.5, len(x))

# Perform regression and create plot
results = plotter.plot_with_regression(
    data=(x, y),
    func=quadratic,
    xlabel=r"$x$ [units]",
    ylabel=r"$y$ [units]",
    title=r"Quadratic Fit: $y = ax^2 + bx + c$",
    filename="my_plot.pdf",
    param_names=("a", "b", "c")
)

print(f"R² = {results['r_squared']:.4f}")
print(f"Parameters: {results['params']}")

API Reference

PhysPlot Class

plot_with_regression(data, func, **kwargs)

Create a plot with regression analysis.

Parameters:

• data: Tuple of (x_values, y_values)
• func: Function to fit (must accept x as first parameter, then fit parameters)
• xlabel, ylabel, title: Plot labels (LaTeX supported)
• filename: Output PDF filename
• param_names: Tuple of parameter names for equation display
• initial_guess: Initial parameter values for fitting

Returns: Dictionary containing:

• params: Fitted parameters
• r_squared: R² value
• params_std: Parameter uncertainties
• covariance: Covariance matrix

Built-in Functions

• linear(x, a, b): y = ax + b
• quadratic(x, a, b, c): y = ax² + bx + c
• cubic(x, a, b, c, d): y = ax³ + bx² + cx + d
• exponential(x, a, b, c): y = a·exp(bx) + c
• power_law(x, a, b): y = ax^b
• gaussian(x, a, mu, sigma): y = a·exp(-(x-μ)²/(2σ²))

Examples

Run the example script to see the library in action:

python example.py

This generates several example plots demonstrating different regression types:

• Linear regression
• Quadratic fitting
• Exponential decay
• Gaussian distribution
• Custom sinusoidal function
• Multiple model comparison

Custom Functions

Define your own functions for regression:

def my_function(x, a, b, c):
    """Custom function: y = a*sin(bx) + c"""
    return a * np.sin(b * x) + c

# Use with the plotter
results = plotter.plot_with_regression(
    data=(x_data, y_data),
    func=my_function,
    initial_guess=(1, 1, 0),  # Good initial guess often helps
    param_names=("A", "f", "offset")
)

LaTeX Formatting

The plotter supports full LaTeX formatting in labels and titles:

xlabel=r"Temperature $T$ [K]"
ylabel=r"Resistance $R$ [$\Omega$]" 
title=r"Arrhenius Plot: $R = R_0 \exp\left(\frac{E_a}{k_B T}\right)$"

Requirements

• Python 3.7+
• NumPy
• Matplotlib
• SciPy
• LaTeX distribution (for text rendering)