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Getting Started#

Installation#

The package can be installed via PyPI.

pip install beautiplot

uv add beautiplot

Alternatively, you can clone the GitHub repository and, depending on whether you want to develop it or not, run the following command(s) in the cloned directory:

uv sync

uv sync --all-groups
uv run pre-commit install

Quick example#

Let's start by importing beautiplot and numpy.

import beautiplot.plot as bp
import numpy as np

Next, we generate some data — a damped oscillation in this case.

t = np.linspace(0, 10, 1000)
y = 5 * np.exp(-t / 2) * np.cos(2 * np.pi * t)

Now, we create our first figure using the newfig function. At first, you need to estimate the margins, but you can adjust them later as needed. If you do not specify any margins, the figure will be trimmed to the axes, and tick labels or axis labels won't be visible.

fig, ax = bp.newfig(left=40, bottom=35)

We then plot the data and label the axes. You can use LaTeX syntax for labels.

ax.plot(t, y, label='Damped Oscillation')
ax.set_xlabel('Time $t$ / s')
ax.set_ylabel('Amplitude $A(t)$ / cm')
bp.legend(ax)

Finally, we save the figure. Since pgf is used as a backend, you cannot use plt.show(). Instead, you must save the figure. Here, we use png for visualization, but for publication-quality figures, you should use pdf.

bp.save_figure(fig, 'damped_oscillation.png')

damped_oscillation.png

This simple example introduces the basic functionality, but beautiplot truly shines in more advanced scenarios.

For more detailed examples see the Tutorials section.

You can find documentation for all available functions and settings in the API reference.