Theoretical Principles & Computational Physics Guide
Pie Chart: Scientific Visualization & Field Mapping
Dr. Alorika Chatterjee & Dr. Anirban Shaw
Science & Physics Learners
Python 3 / NumPy / SciPy
1. Core Principles & Theoretical Framework
Graphical Representation: Translates raw numerical simulation arrays into intuitive 2D/3D graphical curves, vector fields, and heatmaps.
Physical Field Representation: Renders electrostatic equipotentials, fluid streamlines, and phase-space trajectories with exact physical coordinates.
Perceptual Color Mapping: Employs scientifically calibrated colormaps (Viridis, Plasma) to represent continuous scalar potentials without visual artifacting.
2. Mathematical Formulation & Algorithmic Pipeline
Vector field mapping and scalar field potential gradients:
$$ \mathbf{E}(\mathbf{r}) = -\nabla V(\mathbf{r}) = -\left(\frac{\partial V}{\partial x}\hat{i} + \frac{\partial V}{\partial y}\hat{j}\right), \quad \Phi(x, y) = \text{Scalar Height} $$
1. Discretized Meshgrid $(X, Y)$ →
2. Field Evaluation $Z = f(X, Y)$ →
3. Render Plot with Units & Labels
3. Numerical Mechanics & Implementation Logic
- Grid Generation: Create 2D coordinate meshgrids using
np.meshgrid(x, y). - Field Computation: Vectorize mathematical equations across the entire coordinate mesh.
- Styling & Labeling: Add scientific axis labels with physical units, informative legends, and high-contrast colorbars.
4. Relevant Computational Practice Exercises
Exercise 1
Generate the plot for Pie Chart and adjust parameter inputs to observe how the visual field distribution shifts.
Exercise 2
Add proper LaTeX formatting to graph axes, title, and colorbar legends for publication-quality figures.
Exercise 3
Export the generated figure to vector SVG or high-resolution PNG format.
- John D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering (2007).
- Mark Newman, "Computational Physics", University of Michigan (2013).