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Matplotlib Plotting Overview

Sep 21, 2025

Overview

This lecture introduces professional plotting in Python using Matplotlib, covering basic line plots, customization, histograms, advanced subplots, 2D/3D plots, stream and vector fields, image reading, and creating animations.

Getting Started with Matplotlib

  • Import Matplotlib (usually as plt) and Numpy for plotting.
  • Use the scienceplots extension (plt.style.use('science', 'notebook', 'grid')) for professional plot styling.
  • Basic line plot: plt.plot(x, y).

Plot Customization Basics

  • Change line style (e.g., '--' for dashed, 'o' for dots).
  • Adjust color with the color argument (e.g., 'purple').
  • Modify line width and marker size with linewidth and ms.
  • Set plot shape and size with plt.figure(figsize=(x, y)).

Essential Plot Elements

  • Always label axes with plt.xlabel() and plt.ylabel().
  • Add legends for multiple data series with plt.legend(), and customize legend location and font size.
  • Overlay multiple datasets to compare data with theoretical models.

Histograms & Density Plots

  • Create histograms with plt.hist(data).
  • Adjust bin counts via the bins parameter.
  • Normalize with density=True for comparing datasets of different sizes.
  • Use histtype='step' for overlaid line-only histograms.

Advanced Plot Layouts

  • Use fig, ax = plt.subplots(rows, cols, figsize=(x, y)) for multiple plots.
  • Access specific axes with indexing (e.g., axes[0,1]).
  • Set labels and titles per subplot using ax.set_xlabel(), ax.set_ylabel(), and ax.set_title().
  • Add text to plots with ax.text(x, y, 'text', transform=ax.transAxes).

Fine-tuning Appearance

  • Adjust tick label sizes with ax.tick_params(labelsize=number).
  • Add bounding boxes to text with the bbox argument.
  • Customize colors, legend appearance, and subplot spacing.

2D Color and Contour Plots

  • Use plt.contourf(x, y, z) for filled 2D plots; control granularity with levels.
  • Add colorbars (plt.colorbar()) with labels for interpretation.
  • Change color maps via the cmap argument (e.g., 'plasma').
  • Use plt.contour() for lines of constant value, and label contours with plt.clabel().

3D and Advanced Visualization

  • Create 3D plots with projection='3d' in plt.subplots().
  • Plot surfaces with ax.plot_surface(x, y, z, cmap='coolwarm').
  • 3D plots are best as animations to show rotation.

Stream and Vector Field Plots

  • Plot vector fields using ax.streamplot(x, y, u, v).
  • Visualize magnitude with color (color=speed) or variable line width (linewidth=lw).
  • Show specific flow lines by setting seed points.

Images and Animations

  • Read and display images with plt.imread() and plt.imshow().
  • Create animations using matplotlib.animation and save as GIFs.
  • Set up animation by initializing empty plot/artists, updating them in an animate function, and saving with anim.save().

Saving and Exporting Figures

  • Save figures with plt.savefig('filename.png', dpi=200) for high quality.

Key Terms & Definitions

  • Matplotlib — Python library for creating static, animated, and interactive plots.
  • Histogram — A plot showing the distribution of a dataset.
  • Density Plot — Histogram normalized so the area sums to one.
  • Contour Plot — Lines or filled regions representing constant values in 2D data.
  • Stream Plot — Visualization of vector fields showing flow lines.
  • Subplot — Multiple axes within one figure for organized visualizations.
  • Animation — Sequence of frames showing dynamic changes in a plot.

Action Items / Next Steps

  • Practice customizing basic plots using different styles, colors, and markers.
  • Create and overlay histograms with varying bin sizes and normalization.
  • Explore subplots and add annotations or text.
  • Experiment with 2D color maps, contour, and stream plots.
  • Try creating and saving an animated plot or GIF using Matplotlib’s animation module.

Full transcript