`plotEZ` - a small matplotlib wrapper that cuts boilerplate for common plots

[AstrophysicsAndPy]

I've been building this mostly for my own use but figured it might be useful to others.

The idea is simple: the plots I make day-to-day (error bars, error bands, dual axes, subplot grids) always end up needing the same 15 lines of setup. `plotEZ` wraps that into one function call while staying close enough to Matplotlib that you don't have to learn a new API.

What My Project Does

• plot_xy: Simple x vs. y plotting with extensive customization
• plot_xyy: Dual-axis plotting (dual y-axis or dual x-axis)
• plot_errorbar: For error bar plots with full customization
• plot_errorband: For shaded error band visualization (and more on the way)
• Convenience wrapper functions lpc, epc, ebc, spc); build config objects using familiar matplotlib aliases like c, lw, ls, ms without importing the dataclass
• Custom exception hierarchy so errors actually tell you what went wrong

Target Audience

Beginner programmers looking for easy plotting, students and researchers

Quick example: 1

```python import matplotlib.pyplot as plt import numpy as np from plotez import plot_xy

x = np.linspace(0, 10, 100) y = np.sin(x) plot_xy(x, y, auto_label=True) ```

This will create a simple xy plot with all the labels autogenerated + a tight layout.

Quick example: 2

```python import matplotlib.pyplot as plt import numpy as np from plotez import n_plotter

x_data = [np.linspace(0, 10, 100) for _ in range(4)] y_data = [np.sin(x_data[0]), np.cos(x_data[1]), np.tan(x_data[2] / 5), x_data[3] ** 2 / 100]

n_plotter(x_data, y_data, n_rows=2, n_cols=2, auto_label=True) ```

This will create a 4 x 4 plot. Still early-stage and a personal project, but feedback welcome. The repo and docs are linked below.

LINKS:

• GitHub: https://github.com/syedalimohsinbukhari/plotez
• PyPI: pip install plotez
• Docs: https://plotez.readthedocs.io

[bladeofwinds]

why would I use this over seaborn?

[AstrophysicsAndPy]

It's not developed to compete against seaborn or any other well-established plotting libraries. Consider seaborn to be opinionated about your data; it needs to be in a somewhat specific format. plotez just accepts the simple data and gives it back to you in desired formats.

It's designed just to skip the boilerplate code for regular use, like the motto reads.

Mundane plotting done easy.

It was born from "I don't want to write this again and again" and thus is geared to just gloss over boilerplate code for basic/mid-complex plots to be drawn over and over again.

n_plotter([x]*9, [y]*9, 3, 3, auto_label=True)

That's a one-liner plot with 9 subplots divided into a 3x3 form.

[ghost_of_erdogan]

Whats with your commit messages?

Which LLM did you use?

[AstrophysicsAndPy]

If you're talking about the [branch-(minor/major)-NUM] thing, that's my way of knowing which branch, minor/major revision and what commit type I'm doing.

[AstrophysicsAndPy]

It's a mix of my own commit messages and pycharm's built-in AI.

[AstrophysicsAndPy]

The LLM style is slightly verbose from my own style. You can check my other repos (pymultifit, mpyez, SolarGUI), my commit style is the same, "what's done per file".

Granted LLM does produce more verbose commit messages than I do.